Realizing frequency rectangular characteristics using a planar circuit made of a normal conductor material such as a printed circuit board (PCB) is difficult. The reason is that the corners of the frequency response are rounded by the effect of the low unloaded quality factors of the resonators. Rectangular frequency characteristics are generally realized by a low-noise amplifier (LNA) with flat gain characteristics and a high-order bandpass filter (BPF) with resonators having high unloaded quality factors. Here, we use an LNA and a fourth-order flat passband BPF made of a PCB to realize the desired characteristics. We first calculate the signal and noise powers to confirm any effects from insertion loss caused by the BPF. Next, we explain the design and fabrication of an LNA, since no proper LNAs have been developed for this research. Finally, the rectangular frequency characteristics are shown by a circuit combining the fabricated LNA and the fabricated flat passband BPF. We show that rectangular frequency characteristics can be realized using a flat passband BPF technique.

Heterogeneous networks (HetNets) are emerging as an inevitable method to tackle the capacity crunch of the cellular networks. Due to the complicated network environment and a large number of configured parameters, coverage and capacity optimization (CCO) is a challenging issue in heterogeneous cellular networks. By combining the self-optimizing algorithm for radio frequency (RF) parameters with the power control mechanism of small cells, the CCO problem of self-organizing network is addressed in this paper. First, the optimization of RF parameters is solved based on reinforcement learning (RL), where the base station is modeled as an agent that can learn effective strategies to control the tunable parameters by interacting with the surrounding environment. Second, the small cell can autonomously change the state of wireless transmission by comparing its distance from the user equipment with the virtual cell size. Simulation results show that the proposed algorithm can achieve better performance on user throughput compared to different conventional methods.

Research on wireless power transmission technology is being actively conducted, and studies on spatial transmission methods such as SSPS are currently underway for applications such as power transfer to the upper part of steel towers and power transfer to flying objects such as drones. To enable such applications, it is necessary to examine the configuration of the power-transfer and power-receiving antennas and to improve the RF-DC conversion efficiency (hereinafter referred to as conversion efficiency) of the rectifier circuit on the power-receiving antenna. To improve the conversion efficiency, various methods that utilize full-wave rectification rather than half-wave rectification have been proposed. However, these come with problems such as a complicated circuit structure, the need for additional capacitors, the selection of components at high frequencies, and a reduction in mounting yield. In this paper, we propose a method to improve the conversion efficiency by loading a high-impedance microstrip line as a feedback line in part of the rectifier circuit. We analyzed a class-F rectifier circuit using circuit analysis software and found that the conversion efficiency of the conventional configuration was 54.2%, but the proposed configuration was 69.3%. We also analyzed a measuring circuit made with a discrete configuration in the 5.8-GHz band and found that the conversion efficiency was 74.7% at 24dBm input.

In an amusement park, an attraction-visiting route considering the waiting time and traveling time improves visitors' satisfaction and experience. We focus on Ising machines to solve the problem, which are recently expected to solve combinatorial optimization problems at high speed by mapping the problems to Ising models or quadratic unconstrained binary optimization (QUBO) models. We propose a mapping of the visiting-route recommendation problem in amusement parks to a QUBO model for solving it using Ising machines. By using an actual Ising machine, we could obtain feasible solutions one order of magnitude faster with almost the same accuracy as the simulated annealing method for the visiting-route recommendation problem.

Code smells can be detected using tools such as a static analyzer that detects code smells based on source code metrics. Developers perform refactoring activities based on the result of such detection tools to improve source code quality. However, such an approach can be considered as reactive refactoring, i.e., developers react to code smells after they occur. This means that developers first suffer the effects of low-quality source code before they start solving code smells. In this study, we focus on proactive refactoring, i.e., refactoring source code before it becomes smelly. This approach would allow developers to maintain source code quality without having to suffer the impact of code smells. To support the proactive refactoring process, we propose a technique to detect decaying modules, which are non-smelly modules that are about to become smelly. We present empirical studies on open source projects with the aim of studying the characteristics of decaying modules. Additionally, to facilitate developers in the refactoring planning process, we perform a study on using a machine learning technique to predict decaying modules and report a factor that contributes most to the performance of the model under consideration.

Contamination of water resources with pathogenic microorganisms excreted in human feces is a worldwide public health concern. Surveillance of fecal contamination is commonly performed by routine monitoring for a single type or a few types of microorganism(s). To design a feasible routine for periodic monitoring and to control risks of exposure to pathogens, reliable statistical algorithms for inferring correlations between concentrations of microorganisms in water need to be established. Moreover, because pathogens are often present in low concentrations, some contaminations are likely to be under a detection limit. This yields a pairwise left-censored dataset and complicates computation of correlation coefficients. Errors of correlation estimation can be smaller if undetected values are imputed better. To obtain better imputations, we utilize side information and develop a new technique, the asymmetric Tobit model which is an extension of the Tobit model so that domain knowledge can be exploited effectively when fitting the model to a censored dataset. The empirical results demonstrate that imputation with domain knowledge is effective for this task.

In general, since the in-band noise of phase-locked loops (PLLs) is mainly caused by charge pumps (CPs), large-size transistors that occupy a large area are used to improve in-band noise of CPs. With the high demand for low phase noise in recent high-performance communication systems, the issue of the trade-off between occupied area and noise in conventional CPs has become significant. A noise-canceling CP circuit is presented in this paper to mitigate the trade-off between occupied area and noise. The proposed CP can achieve lower noise performance than conventional CPs by performing additional noise cancelation. According to the simulation results, the proposed CP can reduce the current noise to 57% with the same occupied area, or can reduce the occupied area to 22% compared with that of the conventional CPs at the same noise performance. We fabricated a prototype of the proposed CP embedded in a 28-GHz LC-PLL using a 16-nm FinFET process, and 1.2-dB improvement in single sideband integrated phase noise is achieved.

Observations of gravity waves based on High Frequency Surface Wave Radar can make contributions to a better understanding of the energy transfer process between the ocean and the ionosphere. In this paper, through processing the observed data of the ionospheric clutter from HFSWR during the period of the Typhoon Rumbia with short-time Fourier transform method, HFSWR was proven to have the capability of gravity wave detection.

This paper presents an analytical model that yields the unavailability of a network function when each backup server can protect two functions and can recover one of them. Previous work describes a model to deal with the case that each function can be protected only by one server. In our model, we allow each function to be protected by multiple servers to ensure function availability. This requires us to know the feasible states of a connected component and its state transitions. By adopting the divide-and-conquer method, we enumerate the feasible states of a connected component. We then classify its state transitions. Based on the obtained feasible states and the classification of the state transitions, we enumerate the feasible states incoming to and outgoing from a general state, the transfer rates, and the conditions. With those informations, we generate multiple equations about the state transitions. Finally, by solving them, we obtain the probabilities that a connected component is in each state and calculate the unavailability of a function. Numerical results show that the average unavailability of a function is reduced by 18% and 5.7% in our two examined cases by allowing each function to be protected by multiple servers.

In this letter, we will prove that chaotic binary sequences generated by the tent map and Walsh functions are i.i.d. (independent and identically distributed) and orthogonal to each other.

Optical code-division multiple-access (CDMA) techniques provide multi-user data transmission services in optical wireless and fiber communication systems. Several signature codes, such as modified prime sequence codes (MPSCs), generalized MPSCs (GMPSCs) and modified pseudo-orthogonal M-sequence sets, have been proposed for synchronous optical CDMA systems. In this paper, a new scheme is proposed for synchronous optical CDMA to increase the number of users and, consequently, to increase the total data rate without increasing the chip rate. The proposed scheme employs a GMPSC and an extended bi-orthogonal code which is a unipolar code generated from a bipolar Walsh code. Comprehensive comparisons between the proposed scheme and several conventional schemes are shown. Moreover, bit error rate performance and energy efficiency of the proposed scheme are evaluated comparing with those of the conventional optical CDMA schemes under atmospheric propagation environment.

As smartphones have become widespread in the past decade, Wi-Fi signal-based crowd estimation schemes are receiving increased attention. These estimation schemes count the number of unique MAC addresses in Wi-Fi signals, hereafter called probe requests (PRs), instead of counting the number of people. However, these estimation schemes have low accuracy of crowd estimation under MAC address randomization that replaces a unique MAC address with various dummy MAC addresses. To solve this problem, in this paper, we propose an indoor crowd estimation scheme using the number of PRs under MAC address randomization. The main idea of the proposed scheme is to leverage the fact that the number of PRs per a unit of time changes in proportion to the number of smartphones. Since a smartphone tends to send a constant number of PRs per a unit of time, the proposed scheme can estimate the accurate number of smartphones. Various experiment results show that the proposed scheme reduces estimation error by at most 75% compared to the conventional Wi-Fi signal-based crowd estimation scheme in an indoor environment.

Iterative block decision feedback equalization with hard-decision feedback (HD-IBDFE) was proposed for single-carrier transmission with frequency-domain equalization (SC-FDE). The detection performance hinges upon not only error propagation, but also the accuracy of estimating the parameters used to re-compute the equalizer coefficients at each iteration. In this paper, we use the erasure zone (EZ) to de-emphasize the feedback values when the hard decisions are not reliable. EZ use also enables a more accurate, and yet computationally more efficient, parameter estimation method than HD-IBDFE. We show that the resulting equalizer coefficients share the same mathematical form as that of the HD-IBDFE, thereby preserving the merit of not requiring matrix inverse operations in calculating the equalizer coefficients. Simulations show that, by using the EZ and the proposed parameter estimation method, a significant performance improvement over the conventional HD-IBDFE can be achieved, but with lower complexity.

This paper proposes a physical-layer cell identity (PCID) detection method that uses joint estimation of the frequency offset and secondary synchronization signal (SSS) sequence for the 5G new radio (NR) initial access with beamforming transmission at a base station. Computer simulation results show that using the PCID detection method with the proposed joint estimation yields an almost identical PCID detection probability as the primary synchronization signal (PSS) detection probability at an average received signal-to-noise ratio (SNR) of higher than approximately -5dB suggesting that the residual frequency offset is compensated to a sufficiently low level for the SSS sequence estimation. It is also shown that the PCID detection method achieves a high PCID detection probability of greater than 90% and 50% at the carrier frequency of 30 and 50GHz, respectively, at the average received SNR of 0dB for the frequency stability of a user equipment oscillator of 3ppm.

Isogeny-based cryptography, such as commutative supersingular isogeny Diffie-Hellman (CSIDH), have been shown to be promising candidates for post-quantum cryptography. However, their speeds have remained unremarkable. This study focuses on computing odd-degree isogeny between Montgomery curves, which is a dominant computation in CSIDH. Our proposed “2-ADD-Skip method” technique reduces the required number of points to be computed during isogeny computation. A novel algorithm for isogeny computation is also proposed to efficiently utilize the 2-ADD-Skip method. Our proposed algorithm with the optimized parameter reduces computational cost by approximately 12% compared with the algorithm proposed by Meyer and Reith. Further, individual experiments for each degree of isogeny ℓ show that the proposed algorithm is the fastest for 19≤ℓ≤373 among previous studies focusing on isogeny computation including the Õ(√ℓ) algorithm proposed by Bernstein et al. The experimental results also show that the proposed algorithm achieves the fastest on CSIDH-512. For CSIDH-1024, the proposed algorithm is faster than the algorithm by Meyer and Reith although it is slower than the algorithm by Bernstein et al.

Frequency delta sigma modulation (FDSM) is a unique analog to digital conversion technique featuring large dynamic range with wide frequency band width. It can be used for high performance digital-output sensors, if the oscillator in the FDSM is replaced by a variable frequency oscillator whose frequency depends on a certain external physical quantity. One of the most important parameters governing the performance of these sensors is a phase noise of the oscillator. The phase noise is an essential error source in the FDSM, and it is quite important for this type of sensors because they use a high frequency oscillator and an extremely large oversampling ratio. In this paper, we will discuss the quantitative effects of the phase noise on the FDSM output on the basis of a simple model. The model was validated with experiments for three types of oscillators.

Recently, information centric network (ICN) has attracted attention because cached content delivery from router's cache storage improves quality of service (QoS) by reducing redundant traffic. Then, adaptive video streaming is applied to ICN to improve client's quality of experience (QoE). However, in the previous approaches for the cache control, the router implicitly caches the content requested by a user for the other users who may request the same content subsequently. As a result, these approaches are not able to use the cache effectively to improve client's QoE because the cached contents are not always requested by the other users. In addition, since the previous cache control does not consider network congestion state, the adaptive bitrate (ABR) algorithm works incorrectly and causes congestion, and then QoE degrades due to unnecessary congestion. In this paper, we propose an explicit cache placement notification for congestion-aware adaptive video streaming over ICN (CASwECPN) to mitigate congestion. CASwECPN encourages explicit feedback according to the congestion detection in the router on the communication path. While congestion is detected, the router caches the requested content to its cache storage and explicitly notifies the client that the requested content is cached (explicit cache placement and notification) to mitigate congestion quickly. Then the client retrieve the explicitly cached content in the router detecting congestion according to the general procedures of ICN. The simulation experiments show that CASwECPN improves both QoS and client's QoE in adaptive video streaming that adjusts the bitrate adaptively every video segment download. As a result, CASwECPN effectively uses router's cache storage as compared to the conventional cache control policies.

Recently the data-driven learning of dynamic systems has become a promising approach because no physical knowledge is needed. Pure machine learning approaches such as Gaussian process regression (GPR) learns a dynamic model from data, with all physical knowledge about the system discarded. This goes from one extreme, namely methods based on optimizing parametric physical models derived from physical laws, to the other. GPR has high flexibility and is able to model any dynamics as long as they are locally smooth, but can not generalize well to unexplored areas with little or no training data. The analytic physical model derived under assumptions is an abstract approximation of the true system, but has global generalization ability. Hence the optimal learning strategy is to combine GPR with the analytic physical model. This paper proposes a method to learn dynamic systems using GPR with analytic ordinary differential equations (ODEs) as prior information. The one-time-step integration of analytic ODEs is used as the mean function of the Gaussian process prior. The total parameters to be trained include physical parameters of analytic ODEs and parameters of GPR. A novel method is proposed to simultaneously learn all parameters, which is realized by the fully Bayesian GPR and more promising to learn an optimal model. The standard Gaussian process regression, the ODE method and the existing method in the literature are chosen as baselines to verify the benefit of the proposed method. The predictive performance is evaluated by both one-time-step prediction and long-term prediction. By simulation of the cart-pole system, it is demonstrated that the proposed method has better predictive performances.

This paper presents the average block error rate (BLER) performance of circular 32QAM and 64QAM schemes employing a frequency domain equalizer (FDE) for discrete Fourier transform (DFT)-precoded orthogonal frequency division multiplexing (OFDM) in multipath Rayleigh fading channels. The circular QAM scheme has an advantageous feature in that the fluctuation in the amplitude component is smaller than that for the cross or rectangular QAM scheme. Hence, focusing on the actual received signal-to-noise power ratio (SNR) taking into account a realistic peak-to-average power ratio (PAPR) measure called the cubic metric (CM), we compare the average BLER of the circular 32QAM and 64QAM schemes with those of cross 32QAM and rectangular 64QAM schemes, respectively. We investigate the theoretical throughput of various circular 32QAM and 64QAM schemes based on mutual information from the viewpoint of the minimum Euclidean distance. Link-level simulation results show that the circular 32QAM and 64QAM schemes with independent bit mapping for the phase and amplitude modulations achieves a lower required average received SNR considering the CM than that with the minimum Euclidean distance but with composite mapping of the phase and amplitude modulations. Through extensive link-level simulations, we show the potential benefit of the circular 32QAM and 64QAM schemes in terms of reducing the required average received SNR considering the CM that satisfies the target average BLER compared to the cross 32QAM or rectangular 64QAM scheme.

This paper proposes time division multiplexing (TDM) based reference signal (RS) multiplexing for faster-than-Nyquist (FTN) signaling using orthogonal frequency division multiplexing (OFDM). We also propose a subframe structure in which a cyclic prefix (CP) is appended to only the TDM based RS block and the first FTN symbol to achieve accurate estimation of the channel response in a multipath fading channel with low CP overhead. Computer simulation results show that the loss in the required average received SNR satisfying the average block error rate (BLER) of 10-2 using the proposed TDM based RS multiplexing from that with ideal channel estimation is suppressed to within approximately 1.2dB and 1.7dB for QPSK and 16QAM, respectively. This is compared to when the improvement ratio of the spectral efficiency from CP-OFDM is 1.31 with the rate-1/2 turbo code. We conclude that the TDM based RS multiplexing with the associated CP multiplexing is effective in achieving accurate channel estimation for FTN signaling using OFDM.