Quantum key distribution or secret key distribution (SKD) has been studied to deliver a secrete key for secure communications, whose security is physically guaranteed. For practical deployment, such systems are desired to be overlaid onto existing wavelength-multiplexing transmission systems, without using a dedicated transmission line. This study analytically investigates the feasibility of the intensity-modulation/direction-detection (IM/DD) SKD scheme being wavelength-multiplexed with conventional wavelength-division-multiplexed (WDM) signals, concerning spontaneous Raman scattering light from conventional optical signals. Simulation results indicate that IM/DD SKD systems are not degraded when they are overlaid onto practically deployed dense WDM transmission systems in the C-band, owing to the feature of the IM/DD SKD scheme, which uses a signal light with an intensity level comparable to conventional optical signals unlike conventional quantum key distribution schemes.

This paper introduces heuristic approaches and a deep reinforcement learning approach to solve a joint virtual network function deployment and scheduling problem in a dynamic scenario. We formulate the problem as an optimization problem. Based on the mathematical description of the optimization problem, we introduce three heuristic approaches and a deep reinforcement learning approach to solve the problem. We define an objective to maximize the ratio of delay-satisfied requests while minimizing the average resource cost for a dynamic scenario. Our introduced two greedy approaches are named finish time greedy and computational resource greedy, respectively. In the finish time greedy approach, we make each request be finished as soon as possible despite its resource cost; in the computational resource greedy approach, we make each request occupy as few resources as possible despite its finish time. Our introduced simulated annealing approach generates feasible solutions randomly and converges to an approximate solution. In our learning-based approach, neural networks are trained to make decisions. We use a simulated environment to evaluate the performances of our introduced approaches. Numerical results show that the introduced deep reinforcement learning approach has the best performance in terms of benefit in our examined cases.

In this paper, we investigate a wireless-powered relays assisted batteryless IoT network based on the non-linear energy harvesting model, where there exists an energy service provider constituted by the hybrid access point (HAP) and an IoT service provider constituted by multiple clusters. The HAP provides energy signals to the batteryless devices for information backscattering and the wireless-powered relays for energy harvesting. The relays are deployed to assist the batteryless devices with the information transmission to the HAP by using the harvested energy. To model the energy interactions between the energy service provider and IoT service provider, we propose a Stackelberg game based framework. We aim to maximize the respective utility values of the two providers. Since the utility maximization problem of the IoT service provider is non-convex, we employ the fractional programming theory and propose a block coordinate descent (BCD) based algorithm with successive convex approximation (SCA) and semi-definite relaxation (SDR) techniques to solve it. Numerical simulation results confirm that compared to the benchmark schemes, our proposed scheme can achieve larger utility values for both the energy service provider and IoT service provider.

This paper proposes and evaluates machine learning (ML)-based compensation methods for the transmit (Tx) weight matrices of actual singular value decomposition (SVD)-multiple-input and multiple-output (MIMO) transmissions. These methods train ML models and compensate the Tx weight matrices by using a large amount of training data created from statistical distributions. Moreover, this paper proposes simplified channel metrics based on the channel quality of actual SVD-MIMO transmissions to evaluate compensation performance. The optimal parameters are determined from many ML parameters by using the metrics, and the metrics for this determination are evaluated. Finally, a comprehensive computer simulation shows that the optimal parameters improve performance by up to 7.0dB compared with the conventional method.

Measurements of contact discharge current waveforms from an ESD generator with a test voltage of 4kV are conducted with the IEC specified arrangement of a 2m long return current cable in different three calibration environments that all comply with the IEC calibration standard to identify the occurrence source of damped oscillations (ringing), which has remained unclear since contact discharge testing was first adopted in 1989 IEC publication 801-2. Their frequency spectra are analyzed comparing with the spectrum calculated from the ideal contact discharge current waveform without ringing (IEC specified waveform) offered in IEC 61000-4-2 and the spectra derived from a simplified equivalent circuit based on the IEC standard in combination with the measured input impedances of one-ended grounding return current cable with the same arrangement in the same calibration environment as those for the current measurements. The results show that the measured contact discharge waveforms have ringing around the IEC specified waveform after the falling edge of the peak, causing their spectra from 20MHz to 200MHz, but the spectra from 40MHz to 200MHz significantly differ depending on the calibration environments even for the same cable arrangement, which do not almost affect the spectra from 20MHz to 40MHz and over 200MHz. In the calibration environment under the cable arrangement close to the reference ground, the spectral shapes of the measured contact discharge currents and their frequencies of the multiple peaks and dips roughly correspond to the spectral distributions calculated from the simplified equivalent circuit using the measured cable input impedances. These findings reveal that the root cause of ringing is mainly due to the resonances of the return current cable, and calibration environment under the cable arrangement away from the reference ground tends to mitigate the cable resonances.

This paper proposes an adaptive mixing probability scheme for mixed Gibbs sampling (MGS) or MGS with maximum ratio combining (MRC) in multiple-input multiple-output (MIMO) demodulation. In the conventional MGS algorithm, the mixing probability is fixed. Thus, if a search point is captured by a local minimum, it takes a larger number of samples to escape. In the proposed scheme, the mixing probability is increased when a candidate transmit symbol vector is captured by a local minimum. Using the adaptive mixing probability, the numbers of candidate transmit symbol vectors searched by demodulation algorithms increase. The proposed scheme in MGS as well as MGS with MRC reduces an error floor level as compared with the conventional scheme. Numerical results obtained through computer simulation show that the bit error rates of the MGS as well as the MGS with MRC reduces by about 1/100 when the number of iterations is 100 in a 64×64 MIMO system.

This paper proposes a multibeam digital predistorter (DPD) that suppresses intercarrier interference caused by nonlinear distortions of power amplifiers (PAs) while reducing the power consumption of a multibeam array antenna transmitter. The proposed DPD reduces power consumption by allowing the final PAs of the array antenna transmitter to operate in a highly efficient nonlinear mode and compensating for the nonlinear distortions of the PAs with a unified dedicated DPD per subarray. Additionally, it provides the required high-quality signal transmission for high throughputs, such as realizing a 256-quadrature amplitude modulation (QAM) transmission instead of a 64-QAM transmission. Specifically, it adds an inverse-component signal to cancel the interference from an adjacent carrier of another beam. Consequently, it can suppress the intercarrier interference in the beam direction and improve the error vector magnitude (EVM) during the multibeam transmission, in which the frequency bands of the beams are adjacent. The experimental results obtained for two beams at 28.0 and 28.4GHz demonstrate that, compared with the previous single-beam DPD, the proposed multibeam DPD can improve the EVM. Also, they demonstrate that the proposed DPD can achieve an EVM value of <3%, which completely satisfies the 3GPP requirements for a 256-QAM transmission.

To localize an unknown wave source in non-line-of-sight environments, a wave source localization scheme using multiple unmanned-aerial-vehicles (UAVs) is proposed. In this scheme, each UAV estimates the direction-of-arrivals (DoAs) of received signals and the wave source is localized from the estimated DoAs by means of maximum likelihood estimation. In this study, by extending the concept of this scheme, we propose a novel wave source localization scheme using a single UAV. In the proposed scheme, the UAV moves on the path comprising multiple measurement points and the wave source is sequentially localized from DoA distributions estimated at these measurement points. At each measurement point, with a moving path planning algorithm, the UAV determines the next measurement point from the estimated DoA distributions and measurement points that the UAV has already visited. We consider two moving path planning algorithms, and validate the proposed scheme through simulation experiments.

Frame synchronization detection before data transmission is an important module which directly affects the lifetime and coexistence of underwater acoustic communication (UAC) networks, where linear frequency modulation (LFM) is a frame preamble signal commonly used for synchronization. Unlike terrestrial wireless communications, strong bursty noise frequently appears in UAC. Due to the long transmission distance and the low signal-to-noise ratio, strong short-distance bursty noise will greatly reduce the accuracy of conventional fractional fourier transform (FrFT) detection. We propose a multi-segment verification fractional fourier transform (MFrFT) preamble detection algorithm to address this challenge. In the proposed algorithm, 4 times of adjacent FrFT operations are carried out. And the LFM signal identifies by observing the linear correlation between two lines connected in pair among three adjacent peak points, called ‘dual-line-correlation mechanism’. The accurate starting time of the LFM signal can be found according to the peak frequency of the adjacent FrFT. More importantly, MFrFT do not result in an increase in computational complexity. Compared with the conventional FrFT detection method, experimental results show that the proposed algorithm can effectively distinguish between signal starting points and bursty noise with much lower error detection rate, which in turn minimizes the cost of retransmission.

Physical contact (PC) optical connectors realize long-term stability by maintaining contact with the optical fiber even during temperature fluctuations caused by the microscopic displacement of the ferrule endface. With multicore fiber (MCF) connectors, stable PC connection conditions need to be newly investigated because MCFs have cores other than at the center. In this work, we investigated the microscopic displacement of connected ferrule endfaces using the finite element method (FEM). As a result, by using MCF connectors with an apex offset, we found that the allowable fiber undercut where all the cores make contact is slightly smaller than that of single-mode fiber (SMF) connectors. Therefore, we propose a new equation for determining the allowable fiber undercut of MCF connectors. We also fabricated MCF connectors with an allowable fiber undercut and confirmed their reliability using the composite temperature/humidity cyclic test.

The ideal point scattering model requires that each scattering center is isotropic, the position of the scattering center corresponding to the target remains unchanged, and the backscattering amplitude and phase of the target do not change with the incident frequency and incident azimuth. In fact, these conditions of the ideal point scattering model are difficult to meet, and the scattering models are not ideal in most cases. In order to understand the difference between non-ideal scattering center and ideal scattering center, this paper takes a metal plate as the research object, carries out two-dimensional imaging of the metal plate, compares the difference between the imaging position and the theoretical target position, and compares the shape of the scattering center obtained from two-dimensional imaging of the plate from different angles. From the experimental results, the offset between the scattering center position and the theoretical target position corresponding to the two-dimensional imaging of the plate under the non-ideal point scattering model is less than the range resolution and azimuth resolution. The deviation between the small angle two-dimensional imaging position and the theoretical target position using the ideal point scattering model is small, and the ideal point scattering model is still suitable for the two-dimensional imaging of the plate. In the imaging process, the ratio of range resolution and azimuth resolution affects the shape of the scattering center. The range resolution is equal to the azimuth resolution, the shape of the scattering center is circular; the range resolution is not equal to the azimuth resolution, and the shape of the scattering center is elliptic. In order to obtain more accurate two-dimensional image, the appropriate range resolution and azimuth resolution can be considered when using the ideal point scattering model for two-dimensional imaging. The two-dimensional imaging results of the plate at different azimuth and angle can be used as a reference for the study of non-ideal point scattering model.

In the Robot Operating System (ROS), a major middleware for robots, the Transform Library (TF) is a mandatory package that manages transformation information between coordinate systems by using a directed forest data structure and providing methods for registering and computing the information. However, the structure has two fundamental problems. The first is its poor scalability: since it accepts only a single thread at a time due to using a single giant lock for mutual exclusion, the access to the tree is sequential. Second, there is a lack of data freshness: it retrieves non-latest synthetic data when computing coordinate transformations because it prioritizes temporal consistency over data freshness. In this paper, we propose methods based on transactional techniques. This will allow us to avoid anomalies, achieve high performance, and obtain fresh data. These transactional methods show a throughput of up to 429 times higher than the conventional method on a read-only workload and a freshness of up to 1276 times higher than the conventional one on a read-write combined workload.

One of the performance bottlenecks of a processor is the front-end that supplies instructions. Various techniques, such as cache replacement algorithms and hardware prefetching, have been investigated to facilitate smooth instruction supply at the front-end and to improve processor performance. In these approaches, one of the most important factors has been the reduction in the number of instruction cache misses. By using the number of instruction cache misses or derived factors, previous studies have explained the performance improvements achieved by their proposed methods. However, we found that the number of instruction cache misses does not always explain performance changes well in modern processors. This is because the front-end in modern processors handles subsequent instruction cache misses in overlap with earlier ones. Based on this observation, we propose a novel factor: the number of miss regions. We define a region as a sequence of instructions from one branch misprediction to the next, while we define a miss region as a region that contains one or more instruction cache misses. At the boundary of each region, the pipeline is flushed owing to a branch misprediction. Thus, cache misses after this boundary are not handled in overlap with cache misses before the boundary. As a result, the number of miss regions is equal to the number of cache misses that are processed without overlap. In this paper, we demonstrate that the number of miss regions can well explain the variation in performance through mathematical models and simulation results. The results show that the model explains cycles per instruction with an average error of 1.0% and maximum error of 4.1% when applying an existing prefetcher to the instruction cache. The idea of miss regions highlights that instruction cache misses and branch mispredictions interact with each other in processors with a decoupled front-end. We hope that considering this interaction will motivate the development of fast performance estimation methods and new microarchitectural methods.

Annealing computation has recently attracted attention as it can efficiently solve combinatorial optimization problems using an Ising spin-glass model. Stochastic cellular automata annealing (SCA) is a promising algorithm that can realize fast spin-update by utilizing its parallel computing capability. However, in SCA, pinning effect control to suppress the spin-flip probability is essential, making escaping from local minima more difficult than serial spin-update algorithms, depending on the problem. This paper proposes a novel approach called APC-SCA (Autonomous Pinning effect Control SCA), where the pinning effect can be controlled autonomously by focusing on individual spin-flip. The evaluation results using max-cut, N-queen, and traveling salesman problems demonstrate that APC-SCA can obtain better solutions than the original SCA that uses pinning effect control pre-optimized by a grid search. Especially in solving traveling salesman problems, we confirm that the tour distance obtained by APC-SCA is up to 56.3% closer to the best-known compared to the conventional approach.

The Order/Radix Problem (ORP) is an optimization problem that can be solved to find an optimal network topology in distributed memory systems. It is important to find the optimum number of switches in the ORP. In the case of a regular graph, a good estimation of the preferred number of switches has been proposed, and it has been shown that simulated annealing (SA) finds a good solution given a fixed number of switches. However, generally the optimal graph does not necessarily satisfy the regular condition, which greatly increases the computational costs required to find a good solution with a suitable number of switches for each case. This study improved the new method based on SA to find a suitable number of switches. By introducing neighborhood searches in which the number of switches is increased or decreased, our method can optimize a graph by changing the number of switches adaptively during the search. In numerical experiments, we verified that our method shows a good approximation for the best setting for the number of switches, and can simultaneously generate a graph with a small host-to-host average shortest path length, using instances presented by Graph Golf, an international ORP competition.

FPGA cluster is a promising platform for future computing not only in the cloud but in the 5G wireless base stations with limited power supply by taking significant advantage of power efficiency. However, almost no power analyses with real systems have been reported. This work reports the detailed power consumption analyses of two FPGA clusters, namely FiC and M-KUBOS clusters with introducing power measurement tools and running the real applications. From the detailed analyses, we find that the number of activated links mainly determines the total power consumption of the systems regardless they are used or not. To improve the performance of applications while reducing power consumption, we should increase the clock frequency of the applications, use the minimum number of links and apply link aggregation. We also propose the power model for both clusters from the results of the analyses and this model can estimate the total power consumption of both FPGA clusters at the design step with 15% errors at maximum.

Binary Neural Networks (BNN) have binarized neuron and connection values so that their accelerators can be realized by extremely efficient hardware. However, there is a significant accuracy gap between BNNs and networks with wider bit-width. Conventional BNNs binarize feature maps by static globally-unified thresholds, which makes the produced bipolar image lose local details. This paper proposes a multi-input activation function to enable adaptive thresholding for binarizing feature maps: (a) At the algorithm level, instead of operating each input pixel independently, adaptive thresholding dynamically changes the threshold according to surrounding pixels of the target pixel. When optimizing weights, adaptive thresholding is equivalent to an accompanied depth-wise convolution between normal convolution and binarization. Accompanied weights in the depth-wise filters are ternarized and optimized end-to-end. (b) At the hardware level, adaptive thresholding is realized through a multi-input activation function, which is compatible with common accelerator architectures. Compact activation hardware with only one extra accumulator is devised. By equipping the proposed method on FPGA, 4.1% accuracy improvement is achieved on the original BNN with only 1.1% extra LUT resource. Compared with State-of-the-art methods, the proposed idea further increases network accuracy by 0.8% on the Cifar-10 dataset and 0.4% on the ImageNet dataset.

The aim of a computer-aided drawing therapy system in this work is to associate drawings which a client makes with the client's mental state in quantitative terms. A case study is conducted on experimental data which contain both pastel drawings and mental state scores obtained from the same client in a psychotherapy program. To perform such association through colors, we translate a drawing to a color feature by measuring its representative colors as primary color rates. A primary color rate of a color is defined from a psychological primary color in a way such that it shows a rate of emotional properties of the psychological primary color which is supposed to affect the color. To obtain several informative colors as representative ones of a drawing, we define two kinds of color: approximate colors extracted by color reduction, and area-averaged colors calculated from the approximate colors. A color analysis method for extracting representative colors from each drawing in a drawing sequence under the same conditions is presented. To estimate how closely a color feature is associated with a concurrent mental state, we propose a method of utilizing machine-learning classification. A practical way of building a classification model through training and validation on a very small dataset is presented. The classification accuracy reached by the model is considered as the degree of association of the color feature with the mental state scores given in the dataset. Experiments were carried out on given clinical data. Several kinds of color feature were compared in terms of the association with the same mental state. As a result, we found out a good color feature with the highest degree of association. Also, primary color rates proved more effective in representing colors in psychological terms than RGB components. The experimentals provide evidence that colors can be associated quantitatively with states of human mind.

Recently, Global navigation satellite system (GNSS) positioning has been widely used in various applications (e.g. car navigation system, smartphone map application, autonomous driving). In GNSS positioning, coordinates are calculated from observed satellite signals. The observed signals contain various errors, so the calculated coordinates also have some errors. Double-difference is one of the widely used ideas to reduce the errors of the observed signals. Although double-difference can remove many kinds of errors from the observed signals, some errors still remain (e.g. multipath error). In this paper, we define the remaining error as “double-difference-error (DDE)” and propose a method for estimating DDE using machine learning. In addition, we attempt to improve DGNSS positioning by feeding back the estimated DDE. Previous research applying machine learning to GNSS has focused on classifying whether the signal is LOS (Line Of Sight) or NLOS (Non Line Of Sight), and there is no study that attempts to estimate the amount of error itself as far as we know. Furthermore, previous studies had the limitation that their dataset was recorded at only a few locations in the same city. This is because these studies are mainly aimed at improving the positioning accuracy of vehicles, and collecting large amounts of data using vehicles is costly. To avoid this problem, in this research, we use a huge amount of openly available stationary point data for training. Through the experiments, we confirmed that the proposed method can reduce the DGNSS positioning error. Even though the DDE estimator was trained only on stationary point data, the proposed method improved the DGNSS positioning accuracy not only with stationary point but also with mobile rover. In addition, by comparing with the previous (detect and remove) approach, we confirmed the effectiveness of the DDE feedback approach.

In this paper, we apply two methods in machine learning, dropout and semi-supervised learning, to a recently proposed method called CSQ-SDL which uses deep neural networks for evaluating shift quality from time-series measurement data. When developing a new Automatic Transmission (AT), calibration takes place where many parameters of the AT are adjusted to realize pleasant driving experience in all situations that occur on all roads around the world. Calibration requires an expert to visually assess the shift quality from the time-series measurement data of the experiments each time the parameters are changed, which is iterative and time-consuming. The CSQ-SDL was developed to shorten time consumed by the visual assessment, and its effectiveness depends on acquiring a sufficient number of data points. In practice, however, data amounts are often insufficient. The methods proposed here can handle such cases. For the cases wherein only a small number of labeled data points is available, we propose a method that uses dropout. For those cases wherein the number of labeled data points is small but the number of unlabeled data is sufficient, we propose a method that uses semi-supervised learning. Experiments show that while the former gives moderate improvement, the latter offers a significant performance improvement.