High power amplifier technologies for base transceiver stations (BTSs) for the 5th generation (5G) mobile communication systems and so-called beyond 5G (B5G) systems are reviewed. For sub-6, which is categorized into frequency range 1 (FR1) in 5G, wideband Doherty amplifiers are introduced, and a multi-band load modulation amplifier, an envelope tracking amplifier, and a digital power amplifier for B5G are explained. For millimeter wave 5G, which is categorized into frequency range 2 (FR2), GaAs and GaN MMICs operating at around 28GHz are introduced. Finally, future prospect for THz GaN devices is described.

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.

The need for real-time use of human dynamics data is increasing. The technical requirements for this include improved databases for handling a large amount of data as well as highly accurate sensing of people's movements. A bitmap index format has been proposed for high-speed processing of data that spreads in a two-dimensional space. Using the same format is expected to provide a service that searches queries, reads out desired data, visualizes it, and analyzes it. In this study, we propose a coding format that enables human dynamics data to compress it in the target data size, in order to save data storage for successive increase of real-time human dynamics data. In the proposed method, the spatial population distribution, which is expressed by a probability distribution, is approximated and compressed using the one-pixel one-byte data format normally used for image coding. We utilize two kinds of approximation, which are accuracy of probability and precision of spatial location, in order to control the data size and the amount of information. For accuracy of probability, we propose a non-linear mapping method for the spatial distribution, and for precision of spatial location, we propose spatial scalable layered coding to refine the mesh level of the spatial distribution. Also, in order to enable additional detailed analysis, we propose another scalable layered coding that improves the accuracy of the distribution. We demonstrate through experiments that the proposed data approximation and coding format achieve sufficient approximation of spatial population distribution in the given condition of target data size.

We propose a generalization of the rolling guidance filter (RGF) to a similarity-based clustering (SBC) algorithm which can handle general vector data. The proposed RGF-based SBC algorithm makes the similarities between data clearer than the original similarity values computed from the original data. On the basis of the similarity values, we assign cluster labels to data by an SBC algorithm. Experimental results show that the proposed algorithm achieves better clustering result than the result by the naive application of the SBC algorithm to the original similarity values. Additionally, we study the convergence of a unimodal vector dataset to its mean vector.

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.

Effective emergency resource allocation is essential to guarantee a successful emergency disposal, and it has become a research focus in the area of emergency management. Emergency event logs are accumulated in modern emergency management systems and can be analyzed to support effective resource allocation. This paper proposes a novel approach for efficient emergency resource allocation by mining emergency event logs. More specifically, an emergency event log with various attributes, e.g., emergency task name, emergency resource type (reusable and consumable ones), required resource amount, and timestamps, is first formalized. Then, a novel algorithm is presented to discover emergency response process models, represented as an extension of Petri net with resource and time elements, from emergency event logs. Next, based on the discovered emergency response process models, the minimum resource requirements for both reusable and consumable resources are obtained, and two resource allocation strategies, i.e., the Shortest Execution Time (SET) strategy and the Least Resource Consumption (LRC) strategy, are proposed to support efficient emergency resource allocation decision-making. Finally, a chlorine tank explosion emergency case study is used to demonstrate the applicability and effectiveness of the proposed resource allocation approach.

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.

This paper demonstrates 300GHz terahertz wireless communication using CMOS transmitter (TX) and receiver (RX) modules targeting sixth-generation (6G). To extend communication distance, CMOS modules with WR-3.4 waveguide interface and a high-gain antenna of 40dBi Cassegrain antenna are designed, achieving 36Gbps throughput at a 1m communication distance. Besides, in order to support orthogonal frequency-division multiplexing (OFDM), a self-heterodyne architecture is introduced, which effectively cancels the phase noise in multi-carrier modulation. As a proof-of-concept (PoC), the paper successfully demonstrates real-time video transfer at a 10m communication distance using fifth-generation (5G) based OFDM at the 300GHz frequency band.

Home Energy Management Systems (HEMS) contain devices of multiple manufacturers. Also, a large number of groups of devices must be managed according to several clustering situations. Hence, since it is necessary to establish a common secret group key among group members, the group key management scheme of IEEE 802.21 is used. However, no security verification result by formal methods is known. In this paper, we give the first formal verification result of secrecy and authenticity of the group key management scheme of IEEE 802.21 against insider and outsider attacks using ProVerif, which is an automatic verification tool for cryptographic protocols. As a result, we clarify that a spoofing attack by an insider and a replay attack by an outsider are found for the basic scheme, but these attacks can be prevented by using the scheme with the digital signature option.

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.

Standard neural machine translation (NMT) is on the assumption that the document-level context is independent. Most existing document-level NMT approaches are satisfied with a smattering sense of global document-level information, while this work focuses on exploiting detailed document-level context in terms of a memory network. The capacity of the memory network that detecting the most relevant part of the current sentence from memory renders a natural solution to model the rich document-level context. In this work, the proposed document-aware memory network is implemented to enhance the Transformer NMT baseline. Experiments on several tasks show that the proposed method significantly improves the NMT performance over strong Transformer baselines and other related studies.

Visual sentiment analysis has a lot of applications, including image captioning, opinion mining, and advertisement; however, it is still a difficult problem and existing algorithms cannot produce satisfactory results. One of the difficulties in classifying images into emotions is that visual sentiments are evoked by different types of information - visual and semantic information where visual information includes colors or textures, and semantic information includes types of objects evoking emotions and/or their combinations. In contrast to the existing methods that use only visual information, this paper shows a novel algorithm for image emotion recognition that uses both information simultaneously. For semantic features, we introduce an object vector and a word vector. The object vector is created by an object detection method and reflects existing objects in an image. The word vector is created by transforming the names of detected objects through a word embedding model. This vector will be similar among objects that are semantically similar. These semantic features and a visual feature made by a fine-tuned convolutional neural network (CNN) are concatenated. We perform the classification by the concatenated feature vector. Extensive evaluation experiments using emotional image datasets show that our method achieves the best accuracy except for one dataset against other existing methods. The improvement in accuracy of our method from existing methods is 4.54% at the highest.

In recent years FPGA has become popular in CNN acceleration, and many CNN-to-FPGA toolchains are proposed to fast deploy CNN on FPGA. However, for these toolchains, updating CNN network means regeneration of RTL code and re-implementation which is time-consuming and may suffer timing-closure problems. So, we propose HBDCA: a toolchain and corresponding accelerator. The CNN on HBDCA is defined by the content of BRAM. The toolchain integrates UpdateMEM utility of Xilinx, which updates content of BRAM without re-synthesis and re-implementation process. The toolchain also integrates TensorFlow Lite which provides high-accuracy quantization. HBDCA supports 8-bits per-channel quantization of weights and 8-bits per-layer quantization of activations. Upgrading CNN on accelerator means the kernel size of CNN may change. Flexible structure of HBDCA supports kernel-level parallelism with three different sizes (3×3, 5×5, 7×7). HBDCA implements four types of parallelism in convolution layer and two types of parallelism in fully-connected layer. In order to reduce access number to memory, both spatial and temporal data-reuse techniques were applied on convolution layer and fully-connect layer. Especially, temporal reuse is adopted at both row and column level of an Input Feature Map of convolution layer. Data can be just read once from BRAM and reused for the following clock. Experiments show by updating BRAM content with single UpdateMEM command, three CNNs with different kernel size (3×3, 5×5, 7×7) are implemented on HBDCA. Compared with traditional design flow, UpdateMEM reduces development time by 7.6X-9.1X for different synthesis or implementation strategy. For similar CNN which is created by toolchain, HBDCA has smaller latency (9.97µs-50.73µs), and eliminates re-implementation when update CNN. For similar CNN which is created by dedicated design, HBDCA also has the smallest latency 9.97µs, the highest accuracy 99.14% and the lowest power 1.391W. For different CNN which is created by similar toolchain which eliminate re-implementation process, HBDCA achieves higher speedup 120.28X.

We propose a new method for improving the recognition performance of phonemes, speech emotions, and music genres using multi-task learning. When tasks are closely related, multi-task learning can improve the performance of each task by learning common feature representation for all the tasks. However, the recognition tasks considered in this study demand different input signals of speech and music at different time scales, resulting in input features with different characteristics. In addition, a training dataset with multiple labels for all information sources is not available. Considering these issues, we conduct multi-task learning in a sequential training process using input features with a single label for one information source. A comparative evaluation confirms that the proposed method for multi-task learning provides higher performance for all recognition tasks than individual learning for each task as in conventional methods.

Applications of continuous-time (CT) comparator include relaxation oscillators, pulse width modulators, and so on. CT comparator receives a differential input and outputs a strobe ideally when the differential input crosses zero. Unlike the DT comparators with positive feedback circuit, amplifiers consuming static power must be employed in CT comparators to amplify the input signal. Therefore, minimization of comparator delay under the constraint of power consumption often becomes an issue. This paper analyzes transient behavior of a CT comparator. Using “constant delay approximation”, the comparator delay is derived as a function of input slew rate, number of stages of the preamplifier, and device parameters in each block. This paper also discusses optimum design of the CT comparator. The condition for minimum comparator delay is derived with keeping power consumption constant. The results include that the optimum DC gain of the preamplifier is e∼e3 per stage depending on the element which dominates load capacitance of the preamplifier.

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.

Communication systems operating in the millimeter-wave (mmWave) band have the potential to realize ultra-high throughput and ultra-low latency vehicle-to-vehicle (V2V) communications in 5G and beyond wireless networks. Moreover, because of the weak penetration nature of mmWave, one mmWave channel can be reused in all V2V links, which improves the spectrum efficiency. Although the outstanding performance of the mmWave above has been widely acknowledged, there are still some shortcomings. One of the unavoidable defects is multipath interference. Even though the direct interference link cannot penetrate vehicle bodies, other interference degrades the throughput of the mmWave V2V communication. In this paper, we focus on the multipath interference caused by signal reflections from roads and surroundings, where the interference strength varies in road scenarios. Firstly, we analyze the multipath channel models of mmWave V2V with relay in three typical road scenarios (single straight roads, horizontal curves, and slopes). Their interference differences are clarified. Based on the analysis, a novel method of ZigZag antenna configuration is proposed to guarantee the required data rate. Secondly, the performance of the proposed method is evaluated by simulation. It proves that the ZigZag antenna configuration with an optimal antenna height can significantly suppress the destructive interference, and ensure a throughput over 1Gbps comparing to the conventional antenna configuration at 60GHz band. Furthermore, the effectiveness of ZigZag antenna configuration is demonstrated on a single straight road by outdoor experiments.

We consider an asymptotic stabilization problem for a chain of integrators by using an event-triggered controller. The times required between event-triggered executions and controller updates are uncertain, time-varying, and not necessarily small. We show that the considered system can be asymptotically stabilized by an event-triggered gain-scaling controller. Also, we show that the interexecution times are lower bounded and their lower bounds can be manipulated by a gain-scaling factor. Some future extensions are also discussed. An example is given for illustration.

Single image dehazing algorithm based on Dark Channel Prior (DCP) is widely known. More and more image dehazing algorithms based on DCP have been proposed. However, we found that it is more effective to use DCP in the RAW images before the ISP pipeline. In addition, for the problem of DCP failure in the sky area, we propose an algorithm to segment the sky region and compensate the transmission. Extensive experimental results on both subjective and objective evaluation demonstrate that the performance of the modified DCP (MDCP) has been greatly improved, and it is competitive with the state-of-the-art methods.