Based on imperfect channel state information (CSI), the energy efficiency (EE) of downlink distributed antenna systems (DASs) with multiple receive antennas is investigated assuming composite Rayleigh fading channels. A new EE is introduced which is defined as the ratio of the average transmission rate to the total consumed power. According to this definition, an optimal power allocation (PA) scheme is developed for maximizing EE in a DAS subject to the maximum transmit power constraint. It is shown that a PA solution for the constrained EE optimization does exist and is unique. A Newton method based practical iterative algorithm is presented to solve PA. To avoid the iterative calculation, a suboptimal PA scheme is derived by means of the Lambert function, which yields a closed-form PA. The developed schemes include the ones under perfect CSI as special cases, and only need the statistical CSI. Thus, they have low overhead and good robustness. Moreover, the theoretical EE under imperfect CSI is derived for performance evaluation, and the resulting closed-form EE expression is obtained. Simulation results indicate that the theoretical EE can match the corresponding simulated value well, and the developed suboptimal scheme has performance close to optimal one, but with lower complexity.

The problem with distributed representations generated by neural networks is that the meaning of the features is difficult to understand. We propose a new method that gives a specific meaning to each node of a hidden layer by introducing a manually created word semantic vector dictionary into the initial weights and by using paragraph vector models. We conducted experiments to test the hypotheses using a single domain benchmark for Japanese Twitter sentiment analysis and then evaluated the expandability of the method using a diverse and large-scale benchmark. Moreover, we tested the domain-independence of the method using a Wikipedia corpus. Our experimental results demonstrated that the learned vector is better than the performance of the existing paragraph vector in the evaluation of the Twitter sentiment analysis task using the single domain benchmark. Also, we determined the readability of document embeddings, which means distributed representations of documents, in a user test. The definition of readability in this paper is that people can understand the meaning of large weighted features of distributed representations. A total of 52.4% of the top five weighted hidden nodes were related to tweets where one of the paragraph vector models learned the document embeddings. For the expandability evaluation of the method, we improved the dictionary based on the results of the hypothesis test and examined the relationship of the readability of learned word vectors and the task accuracy of Twitter sentiment analysis using the diverse and large-scale benchmark. We also conducted a word similarity task using the Wikipedia corpus to test the domain-independence of the method. We found the expandability results of the method are better than or comparable to the performance of the paragraph vector. Also, the objective and subjective evaluation support each hidden node maintaining a specific meaning. Thus, the proposed method succeeded in improving readability.

Traffic signal phase and timing (TSPaT) information is valuable for various applications, such as velocity advisory systems, navigation systems, collision warning systems, and so forth. In this paper, we focus on learning baseline timing cycle lengths for fixed-time traffic signals. The cycle length is the most important parameter among all timing parameters, such as green lengths. We formulate the cycle length learning problem as a period estimation problem using a sparse set of noisy observations, and propose the most frequent approximate greatest common divisor (MFAGCD) algorithms to solve the problem. The accuracy performance of our proposed algorithms is experimentally evaluated on both simulation data and the real taxi GPS trajectory data collected in Shanghai, China. Experimental results show that the MFAGCD algorithms have better sparsity and outliers tolerant capabilities than existing cycle length estimation algorithms.

To prevent constraints or defects of a single sensor from malfunctions, this paper proposes a fire detection system based on the Dempster-Shafer theory with multi-sensor technology. The proposed system operates in three stages: measurement, data reception and alarm activation, where an Arduino is tasked with measuring and interpreting the readings from three types of sensors. Sensors under consideration involve smoke, light and temperature detection. All the measured data are wirelessly transmitted to the backend Raspberry Pi for subsequent processing. Within the system, the Raspberry Pi is used to determine the probability of fire events using the Dempster-Shafer theory. We investigate moderate settings of the conflict coefficient and how it plays an essential role in ensuring the plausibility of the system's deduced results. Furthermore, a MySQL database with a web server is deployed on the Raspberry Pi for backlog and data analysis purposes. In addition, the system provides three notification services, including web browsing, smartphone APP, and short message service. For validation, we collected the statistics from field tests conducted in a controllable and safe environment by emulating fire events happening during both daytime and nighttime. Each experiment undergoes the No-fire, On-fire and Post-fire phases. Experimental results show an accuracy of up to 98% in both the No-fire and On-fire phases during the daytime and an accuracy of 97% during the nighttime under reasonable conditions. When we take the three phases into account, the accuracy in the daytime and nighttime increase to 97% and 89%, respectively. Field tests validate the efficiency and accuracy of the proposed system.

Given two data sets of user preferences and product attributes in addition to a set of query products, the aggregate reverse rank (ARR) query returns top-k users who regard the given query products as the highest aggregate rank than other users. ARR queries are designed to focus on product bundling in marketing. Manufacturers are mostly willing to bundle several products together for the purpose of maximizing benefits or inventory liquidation. This naturally leads to an increase in data on users and products. Thus, the problem of efficiently processing ARR queries become a big issue. In this paper, we reveal two limitations of the state-of-the-art solution to ARR query; that is, (a) It has poor efficiency when the distribution of the query set is dispersive. (b) It has to process a large portion user data. To address these limitations, we develop a cluster-and-process method and a sophisticated indexing strategy. From the theoretical analysis of the results and experimental comparisons, we conclude that our proposals have superior performance.

Malware phylogeny refers to inferring evolutionary relationships between instances of families. It has gained a lot of attention over the past several years, due to its efficiency in accelerating reverse engineering of new variants within families. Previous researches mainly focused on tree-based models. However, those approaches merely demonstrate lineage of families using dendrograms or directed trees with rough evolution information. In this paper, we propose a novel malware phylogeny construction method taking advantage of persistent phylogeny tree model, whose nodes correspond to input instances and edges represent the gain or lost of functional characters. It can not only depict directed ancestor-descendant relationships between malware instances, but also show concrete function inheritance and variation between ancestor and descendant, which is significant in variants defense. We evaluate our algorithm on three malware families and one benign family whose ground truth are known, and compare with competing algorithms. Experiments demonstrate that our method achieves a higher mean accuracy of 61.4%.

This paper proposes a deep learning-based non-intrusive objective speech intelligibility estimation method based on recurrent neural network (RNN) with long short-term memory (LSTM) structure. Conventional non-intrusive estimation methods such as standard P.563 have poor estimation performance and lack of consistency, especially, in various noise and reverberation environments. The proposed method trains the LSTM RNN model parameters by utilizing the STOI that is the standard intrusive intelligibility estimation method with reference speech signal. The input and output of the LSTM RNN are the MFCC vector and the frame-wise STOI value, respectively. Experimental results show that the proposed objective intelligibility estimation method outperforms the conventional standard P.563 in various noisy and reverberant environments.

In this letter, we propose a sequential convolutional residual network, where we first analyze a tangled network architecture using simplified equations and determine the critical point to untangle the complex network architecture. Although the residual network shows good performance, the learning efficiency is not better than expected at deeper layers because the network is excessively intertwined. To solve this problem, we propose a network in which the information is transmitted sequentially. In this network architecture, the neighboring layer output adds the input of the current layer and iteratively passes its result to the next sequential layer. Thus, the proposed network can improve the learning efficiency and performance by successfully mitigating the complexity in deep networks. We show that the proposed network performs well on the Cifar-10 and Cifar-100 datasets. In particular, we prove that the proposed method is superior to the baseline method as the depth increases.

This paper improves radiation patterns and impedance matching of a broadband sleeve dipole antenna. A broadband sleeve dipole antenna is designed and the effect of the structure parameters on the |S11| characteristics is calculated. Current distributions of the resonance frequencies are calculated. A broadband sleeve dipole antenna with plate element is proposed. Better impedance matching is obtained by adjusting the size of the plate element. The nulls of the radiation patterns are reduced at high frequencies and the gain in the horizontal direction is improved.

Stochastic resonance can improve the signal-to-noise ratio of digital baseband signals. However, the output of SR system needs some time for evolution to achieve global steady-state. This paper first analyzes the evolution time of SR systems, which is an important factor for digital baseband signal processing based on SR. This investigation shows that the sampling number per symbol should be rather large, and the minimum sampling number per symbol is deduced according to the evolution time of SR system.

This paper studies a wireless powered communication network (WPCN) with non-orthogonal multiple access (NOMA) under successive interference cancellation (SIC) constraints, where the users first harvest energy from the power station and then transmit data to the information receiver simultaneously. Under this setup, we investigate the system throughput maximization problem. We first formulate an optimization problem for a general case, which is non-convex. To derive the optimal solution, new variables are introduced to transform the initial problem into a convex optimization problem. For a special case, i.e., two-user case, the optimal solution is derived as a closed-form expression. Simulations on the effect of SIC constraints show the importance of the distinctness among users' channels for the proposed model.

Performance based on multi-party discussion has been reported to be superior to that based on individuals. However, it is impossible that all participants simultaneously express opinions due to the time and space limitations in a large-scale discussion. In particular, only a few representative discussants and audiences can speak in conventional unidirectional discussions (e.g., panel discussion), although many participants gather for the discussion. To solve these problems, in this study, we proposed a cyber-physical discussion using “COLLAGREE,” which we developed for building consensus of large-scale online discussions. COLLAGREE is equipped with functions such as a facilitator, point ranking system, and display of discussion in tree structure. We focused on the relationship between satisfaction with the discussion and participants' desire to express opinions. We conducted the experiment in the panel discussion of an actual international conference. Participants who were audiences in the floor used COLLAGREE during the panel discussion. They responded to questionnaires after the experiment. The main findings are as follows: (1) Participation in online discussion was associated with the satisfaction of the participants; (2) Participants who desired to positively express opinions joined the cyber-space discussion; and (3) The satisfaction of participants who expressed opinions in the cyber-space discussion was higher than those of participants who expressed opinions in the real-space discussion and those who did not express opinions in both the cyber- and real-space discussions. Overall, active behaviors in the cyber-space discussion were associated with participants' satisfaction with the entire discussion, suggesting that cyberspace provided useful alternative opportunities to express opinions for audiences who used to listen to conventional unidirectional discussions passively. In addition, a complementary relationship exists between participation in the cyber-space and real-space discussions. These findings can serve to create a user-friendly discussion environment.

Recently, the join processing of large-scale datasets in MapReduce environments has become an important issue. However, the existing MapReduce-based join algorithms suffer from too much overhead for constructing and updating the data index. Moreover, the similarity computation cost is high because the existing algorithms partition data without considering the data distribution. In this paper, we propose two grid-based join algorithms for MapReduce. First, we propose a similarity join algorithm that evenly distributes join candidates using a dynamic grid index, which partitions data considering data density and similarity threshold. We use a bottom-up approach by merging initial grid cells into partitions and assigning them to MapReduce jobs. Second, we propose a k-NN join query processing algorithm for MapReduce. To reduce the data transmission cost, we determine an optimal grid cell size by considering the data distribution of randomly selected samples. Then, we perform kNN join by assigning the only related join data to a reducer. From performance analysis, we show that our similarity join query processing algorithm and our k-NN join algorithm outperform existing algorithms by up to 10 times, in terms of query processing time.

Recently, the opportunity to discuss topics on a variety of online discussion bulletin boards has been increasing. However, it can be difficult to understand the contents of each discussion as the number of posts increases. Therefore, it is important to generate headlines that can automatically summarize each post in order to understand the contents of each discussion at a glance. In this paper, we propose a method to extract and generate post headlines for online discussion bulletin boards, automatically. We propose templates with multiple patterns to extract important sentences from the posts. In addition, we propose a method to generate headlines by matching the templates with the patterns. Then, we evaluate the effectiveness of our proposed method using questionnaires.

The intermediate frequency-over-fiber (IFoF) technology has attracted attention as an alternative transmission scheme to the functional split for the next-generation mobile fronthaul links due to its high spectral efficiency and perfect centralized control ability. In this paper, we discuss and clarify network architectures suited for IFoF, based on its advantages over the functional split. One of the major problems for IFoF transmission is dispersion-induced RF power fading, which limits capacity and transmission distance. We introduce our previous work, in which high-capacity and long-distance IFoF transmission was demonstrated by utilizing a parallel intensity/phase modulators (IM/PM) transmitter which can effectively avoid the fading. The IFoF technology with the proposed scheme is well suited for the long-distance mobile fronthaul links for the 5th generation (5G) mobile system and beyond.

Massive multiple-input and multiple-output (MIMO) is a key technology to meet the increasing capacity demands that must be satisfied by next generation wireless systems. However, it is expensive to use linear power amplifiers when implementing a massive MIMO system as it will have hundreds of antennas. In this paper, considering that low peak-to-average power ratio (PAPR) of transmit signals can facilitate hardware-friendly equipment with nonlinear but power-efficient amplifiers, we first formulate the precoding scheme as a PAPR minimization problem. Then, in order to obtain the optimal solution with low complexity, the precoding problem is recast into a Bayesian estimation problem by leveraging belief propagation algorithm. Eventually, we propose a low-PAPR approximate message passing (LP-AMP) algorithm based on belief propagation to ensure the good transmission performance and minimize the PAPR to realize practical deployments. Simulation results reveal that the proposed method can get PAPR reduction and adequate transmission performance, simultaneously, with low computational complexity. Moreover, the results further indicate that the proposed method is suitable for practical implementation, which is appealing for massive multiuser MIMO (MU-MIMO) systems.

This paper demonstrates the suppressing power of 10 Gbps On Off keyed signal using biased half-wave rectification. Authors have previously reported that radio frequency (RF) and optical on-off keying (OOK) signal can be simultaneously transmitted over the radio over fiber (RoF) link [1]. Since the optical OOK signal has much broader bandwidth compared to RF signal, it interferes with RF signal. Reference [1] experimentally shows that the optical OOK signal degrades the RF signal in terms of signal-to-noise power ratio (SNR) when 10 Gbps OOK and 1.9 GHz microwave are employed as baseband and RF, respectively. This paper proposes an interference suppression, and the proposal is subsequently used for detecting the RF signal. Experiments are conducted for the purpose of the proof-of-concept of the proposal. Finally numerical simulations are employed to show the performance enhancement in terms of error vector magnitude (EVM).

This paper presents a 27.5-29.6GHz fractional-N frequency synthesizer using reference and frequency doublers to achieve low in-band and out-of-band phase-noise for 5G mobile communications. A consideration of the baseband carrier recovery circuit helps estimate phase noise requirement for high modulation scheme. The push-push amplifier and 28GHz balun help achieving differential signals with low out-of-band phase noise while consuming low power. A charge pump with gated offset as well as reference doubler help reducing PD noise resulting in low in-band phase noise while sampling loop filter helps reduce spurs. The proposed synthesizer has been implemented in 65nm CMOS technology achieving an in-band and out-of-band phase noise of -78dBc/Hz and -126dBc/Hz, respectively. It consumes only a total power of 33mW. The jitter-power figure-of-merit (FOM) is -231dB which is the highest among the state of the art >20GHz fractional-N PLLs using a low reference clock (<200MHz). The measured reference spurs are less than -80dBc.

In the process of VLSI design, ECO (Engineering Change Order) may occur at any design phase. When ECO happens after the netlist is generated and optimized, designers may like to modify the netlist directly. This is because if ECO is performed in the high-level description, the netlist should be resynthesized and the result may be significantly different from the original one, even if the modification in the high-level description is small. As the result, the efforts spent on optimization so far may become useless. When the netlist is modified directly, the C description should be revised accordingly. This paper proposes a method to reconstruct a C description from the revised netlist. In the proposed method, designers need to provide a template represented in C, which has some vacant (blanked) places and is created from the original C description. The vacant places are automatically synthesized using a CEGIS-based method (Counter Example Guided Inductive Synthesis). Using a set of use-cases, our method tries to find the correct expressions for the vacant places so that the entire description becomes functionally equivalent to the given modified netlist, by only simulating the netlist. Experimental results show that the proposed method can reconstruct C descriptions successfully within practical time for several examples including the one having around 9,000 lines of executable statements. Moreover, the proposed method can be applied to equivalence checking between a netlist and a C description, as shown by our experimental results.

Online chat systems, e.g.., Twitter and Slack, have been used in academic conferences or study meetings as a means of instant discussion and sharing related information alongside a real presentation. We propose a system for activating online discussion by providing a bot that suggests webpages related to current timeline of the discussion. Our system generates keyword vectors according to discussion timeline, searches best related webpages from several web sites, and timely provides these pages to the discussion timeline. This paper describes deployments of our system in two types of meetings: lightning talk format meetings and group meetings; and daily exchanges using online chat system. As a result, we could not find good enough reactions to the bot's postings from meeting participants at the lightning talk format meetings, but we could observe more reactions and progress of discussion caused by the bot's postings at the relaxed meetings and daily exchanges among group members.