In this brief paper, we examine polarimetric scattering characteristics for understanding seasonal change of paddy rice growth by using quad-polarimetric synthetic aperture radar (SAR) data in the X-band. Here we carry out polarimetric scattering measurement for a simplified paddy rice model in an anechoic chamber at X-band frequency to acquire the the quad polarimetric SAR data from the model. The measurements are performed several times for each growth stage of the paddy rice corresponding to seasonal change. The model-based scattering power decomposition is used for the examination of polarimetric features of the paddy rice model. It is found from the result of the polarimetric SAR image analysis for the measurement data that the growth state of the paddy rice in each stage can be understood by considering the ratio of the decomposition powers, when the planting direction of the paddy rice is not only normal but also oblique to radar direction. We can also see that orientation angle compensation (OAC) is useful for improving the accuracy of the growth stage observation in late vegetative stage for oblique planting case.

This article presents a 28GHz high-accuracy phase and amplitude detection circuit for dual-polarized phased-array calibration. With dual-polarized calibration scheme, external LO signal is not required for calibration. The proposed detection circuit detects phase and amplitude independently, using PDC and ADC. By utilizing a 28GHz-to-140kHz downconversion scheme, the phase and amplitude are detected more accurately. In addition, reference signal for PDC and ADC is generated from 28GHz LO signal with divide-by-6 dual-step-mixing injection locked frequency divider (ILFD). This ILFD achieves 24.5-32.5GHz (28%) locking range with only 3.0mW power consumption and 0.01mm2 area. In the measurement, the detection circuit achieves phase and amplitude detections with RMS errors of 0.17degree and 0.12dB, respectively. The total power consumption of the proposed circuit is 59mW with 1-V supply voltage.

Influential user detection is critical in supporting the human facilitator-based facilitation in the online forum. Traditional approaches to detect influential users in the online forum focus on the statistical activity information such as the number of posts. However, statistical activity information cannot fully reflect the influence that users bring to the online forum. In this paper, we propose to detect the influencers from the influence propagation perspective and focus on the influential maximization (IM) problem which aims at choosing a set of users that maximize the influence propagation from the entire social network. An online forum influence propagation network (OFIPN) is proposed to model the influence from an individual user perspective and influence propagation between users, and a heuristic algorithm that is proposed to find influential users in OFIPN. Experiments are conducted by simulations with a real-world social network. Our empirical results show the effectiveness of the proposed algorithm.

As the Internet has become prevalent, the popularity of net media has been growing, to a point that it has taken over conventional mass media. However, TWtrends, the Twitter trends visualization system operated by our research team since 2019, indicates that many topics on TV programs frequently appear on Twitter trendlines. This study investigates the relationship between Twitter and TV programs by collecting information on Twitter trends and TV programs simultaneously. Although this study provides a rough estimation of the volume of tweets that mention TV programs, the results show that several tweets mention TV programs at a constant rate, which tends to increase on the weekend. This tendency of TV-related tweets stems from the audience rating survey results. Considering the study outcome, and the fact that many TV programs introduce topics popular in social media, implies codependency between Internet media (social media) and mass media.

In recent years, with the aging of society, many kinds of research have been actively conducted to recognize human activity in a home to watch over the elderly. Multiple sensors for activity recognition are used. However, we need to consider privacy when using these sensors. One of the candidates of the sensors that keep privacy is a sound sensor. MFCC (Mel-Frequency Cepstral Coefficient) is widely used as a feature extraction algorithm for voice recognition. However, it is not suitable to apply conventional MFCC to activity recognition by sounds of daily life. We denote “sounds of daily life” as “life sounds” simply in this paper. The reason is that conventional MFCC does not extract well several features of life sounds that appear at high frequencies. This paper proposes the improved MFCC and reports the evaluation results of activity recognition by machine learning SVM (Support Vector Machine) using features extracted by improved MFCC.

Annealing machines such as quantum annealing machines and semiconductor-based annealing machines have been attracting attention as an efficient computing alternative for solving combinatorial optimization problems. They solve original combinatorial optimization problems by transforming them into a data structure called an Ising model. At that time, the bit-widths of the coefficients of the Ising model have to be kept within the range that an annealing machine can deal with. However, by reducing the Ising-model bit-widths, its minimum energy state, or ground state, may become different from that of the original one, and hence the targeted combinatorial optimization problem cannot be well solved. This paper proposes an effective method for reducing Ising model's bit-widths. The proposed method is composed of two processes: First, given an Ising model with large coefficient bit-widths, the shift method is applied to reduce its bit-widths roughly. Second, the spin-adding method is applied to further reduce its bit-widths to those that annealing machines can deal with. Without adding too many extra spins, we efficiently reduce the coefficient bit-widths of the original Ising model. Furthermore, the ground state before and after reducing the coefficient bit-widths is not much changed in most of the practical cases. Experimental evaluations demonstrate the effectiveness of the proposed method, compared to existing methods.

Common schedulers for long-term running services that perform task-level optimization fail to accommodate short-living batch processing (BP) jobs. Thus, many efficient job-level scheduling strategies are proposed for BP jobs. However, the existing scheduling strategies perform time-consuming objective optimization which yields non-negligible scheduling delay. Moreover, they tend to assign BP jobs in a centralized manner to reduce monetary cost and synchronization overhead, which can easily cause resource contention due to the task co-location. To address these problems, this paper proposes TEBAS, a time-efficient balance-aware scheduling strategy, which spreads all tasks of a BP job into the cluster according to the resource specifications of a single task based on the observation that computing tasks of a BP job commonly possess similar features. The experimental results show the effectiveness of TEBAS in terms of scheduling efficiency and load balancing performance.

The problem of Isomorphism of Polynomials (IP problem) is known to be important to study the security of multivariate public key cryptosystems, one of the major candidates of post-quantum cryptography, against key recovery attacks. In these years, several schemes based on the IP problem itself or its generalization have been proposed. At PQCrypto 2020, Santoso introduced a generalization of the problem of Isomorphism of Polynomials, called the problem of Blockwise Isomorphism of Polynomials (BIP problem), and proposed a new Diffie-Hellman type encryption scheme based on this problem with Circulant matrices (BIPC problem). Quite recently, Ikematsu et al. proposed an attack called the linear stack attack to recover an equivalent key of Santoso's encryption scheme. While this attack reduced the security of the scheme, it does not contribute to solving the BIPC problem itself. In the present paper, we describe how to solve the BIPC problem directly by simplifying the BIPC problem due to the conjugation property of circulant matrices. In fact, we experimentally solved the BIPC problem with the parameter, which has 256 bit security by Santoso's security analysis and has 72.7bit security against the linear stack attack, by about 10 minutes.

Backscatter communication is an emerging wireless access technology to realize ultra-low power terminals exploiting the modulated reflection of incident radio wave. This paper proposes a method to measure the phase angle of backscatter link using principal component analysis (PCA). The phase angle measurement of backscatter link at the receiver is essential to maximize the signal quality for subsequent demodulation and to measure the distance and the angle of arrival. The drawback of popular phase angle measurement with naive phase averaging and linear regression analysis is to produce erroneous phase angle, where the phase angle is close to $pmrac{pi}{2}$ radian and the signal quality is poor. The advantage of the proposal is quantified with a computer simulation, a conducted experiment and radio propagation experiments.

This study proposes a heterogeneous integration of precise and approximate storage in data center storage. The storage control engine allocates precise and error-tolerant applications to precise and approximate storage, respectively. The appropriate use of both precise and approximate storage is examined by applying a non-volatile memory capacity algorithm. To respond to the changes in application over time, the non-volatile memory capacity algorithm changes capacity of storage class memories (SCMs), namely the memory-type SCM (M-SCM) and storage-type SCM (S-SCM), in non-volatile memory resource. A three-dimensional triple-level cell (TLC) NAND flash is used as a large capacity memory. The results indicate that precise storage exhibits a high performance when the maximum storage cost is high. By contrast, with a low maximum storage cost, approximate storage exhibits high performance using a low bit cost approximate multiple-level cell (MLC) S-SCM.

The reliability of deep neural networks (DNN) against hardware errors is essential as DNNs are increasingly employed in safety-critical applications such as automatic driving. Transient errors in memory, such as radiation-induced soft error, may propagate through the inference computation, resulting in unexpected output, which can adversely trigger catastrophic system failures. As a first step to tackle this problem, this paper proposes constructing a vulnerability model (VM) with a small number of fault injections to identify vulnerable model parameters in DNN. We reduce the number of bit locations for fault injection significantly and develop a flow to incrementally collect the training data, i.e., the fault injection results, for VM accuracy improvement. We enumerate key features (KF) that characterize the vulnerability of the parameters and use KF and the collected training data to construct VM. Experimental results show that VM can estimate vulnerabilities of all DNN model parameters only with 1/3490 computations compared with traditional fault injection-based vulnerability estimation.

The approximate logarithmic multiplier proposed by Mitchell provides an efficient alternative for processing dense multiplication or multiply-accumulate operations in applications such as image processing and real-time robotics. It offers the advantages of small area, high energy efficiency and is suitable for applications that do not necessarily achieve high accuracy. However, its maximum error of 11.1% makes it challenging to deploy in applications requiring relatively high accuracy. This paper proposes a novel operand decomposition method (OD) that decomposes one multiplication into the sum of multiple approximate logarithmic multiplications to widely reduce Mitchell multiplier errors while taking full advantage of its area savings. Based on the proposed OD method, this paper also proposes an accuracy reconfigurable multiply-accumulate (MAC) unit that provides multiple reconfigurable accuracies with high parallelism. Compared to a MAC unit consisting of accurate multipliers, the area is significantly reduced to less than half, improving the hardware parallelism while satisfying the required accuracy for various scenarios. The experimental results show the excellent applicability of our proposed MAC unit in image smoothing and robot localization and mapping application. We have also designed a prototype processor that integrates the minimum functionality of this MAC unit as a vector accelerator and have implemented a software-level accuracy reconfiguration in the form of an instruction set extension. We experimentally confirmed the correct operation of the proposed vector accelerator, which provides the different degrees of accuracy and parallelism at the software level.

From edge devices to cloud servers, providing optimized hardware acceleration for specific applications has become a key approach to improve the efficiency of computer systems. Traditionally, many systems employ commercial field-programmable gate arrays (FPGAs) to implement dedicated hardware accelerator as the CPU's co-processor. However, commercial FPGAs are designed in generic architectures and are provided in the form of discrete chips, which makes it difficult to meet increasingly diversified market needs, such as balancing reconfigurable hardware resources for a specific application, or to be integrated into a customer's system-on-a-chip (SoC) in the form of embedded FPGA (eFPGA). In this paper, we propose an eFPGA generation suite with customizable architecture and integrated development environment (IDE), which covers the entire eFPGA design generation, testing, and utilization stages. For the eFPGA design generation, our intellectual property (IP) generation flow can explore the optimal logic cell, routing, and array structures for given target applications. For the testability, we employ a previously proposed shipping test method that is 100% accurate at detecting all stuck-at faults in the entire FPGA-IP. In addition, we propose a user-friendly and customizable Web-based IDE framework for the generated eFPGA based on the NODE-RED development framework. In the case study, we show an eFPGA architecture exploration example for a differential privacy encryption application using the proposed suite. Then we show the implementation and evaluation of the eFPGA prototype with a 55nm test element group chip design.

Electromagnetic scattering in a coaxial cable having two flanges and concentric grooves is studied. The associated Weber-Orr transform is used to represent electromagnetic fields in an infinitely long cavity, and the mode-matching method is used to enforce boundary continuity. S-parameters obtained by our approach are compared with the reference solutions, and the characteristics are discussed when geometric parameters are varied. The results show that the proposed model provides cost effective and accurate solutions to the problem.

Crowdsensing-based spectrum detection (CSD) is promising to enable full-coverage radio resource availability for the increasingly connected machines in the Internet of Things (IoT) networks. The current CSD scheme consumes a lot of energy and network resources for local sensing, processing, and distributed data reporting for each crowdsensing device. Furthermore, when the amount of reported data is large, the data fusion implemented at the requestor can easily cause high latency. For improving efficiencies in both energy and network resources, this paper proposes a green CSD (GCSD) paradigm. The ambient backscatter (AmB) is used to enable a battery-free mode of operation in which the received spectrum data is reported directly through backscattering without local processing. The energy for backscattering can be provided by ambient radio frequency (RF) sources. Then, relying on air computation (AirComp), the data fusion can be implemented during the backscattering process and over the air by utilizing the summation property of wireless channel. This paper illustrates the model and the implementation process of the GCSD paradigm. Closed-form expressions of detection metrics are derived for the proposed GCSD. Simulation results verify the correctness of the theoretical derivation and demonstrate the green properties of the GCSD paradigm.

Aiming at the problem of the deterioration of the contact performance caused by the wear debris generated during the fretting wear of the electrical connector, low-frequency fretting wear experiments were carried out on the contacts of electrical connectors, the accumulation and distribution of the wear debris were detected by the electrical capacitance tomography technology; the influence of fretting cycles, vibration direction, vibration frequency and vibration amplitude on the accumulation and distribution of wear debris were analyzed; the correlation between characteristic value of wear debris and contact resistance value was studied, and a performance degradation model based on the accumulation and distribution of wear debris was built. The results show that fretting wear and performance degradation are the most serious in axial vibration; the characteristic value of wear debris and contact resistance are positively correlated with the fretting cycles, vibration frequency and vibration amplitude; there is a strong correlation between the sum of characteristic value of wear debris and the contact resistance value; the prediction error of ABC-SVR model of fretting wear performance degradation of electrical connectors constructed by the characteristic value of wear debris is less than 6%. Therefore, the characteristic value of wear debris in contact subareas can quantitatively describe the degree of fretting wear and the process of performance degradation.

We attempt to design and fabricate of a 4×4 Butler matrix for short-millimeter-wave frequencies by using the microfabrication process for a polytetrafluoroethylene (PTFE) substrate-integrated waveguide (SIW) by the synchrotron radiation (SR) direct etching of PTFE and the addition of a metal film by sputter deposition. First, the dimensions of the PTFE SIW using rectangular through-holes for G-band (140-220 GHz) operation are determined, and a cruciform 90 ° hybrid coupler and an intersection circuit are connected by the PTFE SIW to design the Butler matrix. Then, a trial fabrication is performed. Finally, the validity of the design result and the fabrication process is verified by measuring the radiation pattern.

Recently, we introduced a new automata model, so-called colored finite automata (CFAs) whose accepting states are multi-colored (i.e., not conventional black-and-white acceptance) in order to classify their input strings into two or more languages, and solved the specific complexity problems concerning color-unmixedness of nondeterministic CFA. That is, so-called UV, UP, and UE problems are shown to be NLOG-complete, P, and NP-complete, respectively. In this paper, we apply the concept of colored accepting mechanism to pushdown automata and show that the corresponding versions of the above-mentioned complexity problems are all undecidable. We also investigate the case of unambiguous pushdown automata and show that one of the problems turns out to be permanent true (the others remain undecidable).

Stereo depth estimation has become an attractive topic in the computer vision field. Although various algorithms strive to optimize the speed and the precision of estimation, the energy cost of a system is also an essential metric for an embedded system. Among these various algorithms, Semi-Global Matching (SGM) has been a popular choice for some real-world applications because of its accuracy-and-speed balance. However, its power consumption makes it difficult to be applied to an embedded system. Thus, we propose a robust stereo matching system, RT-libSGM, working on the Xilinx Field-Programmable Gate Array (FPGA) platforms. The dedicated design of each module optimizes the speed of the entire system while ensuring the flexibility of the system structure. Through an evaluation on a Zynq FPGA board called M-KUBOS, RT-libSGM achieves state-of-the-art performance with lower power consumption. Compared with the benchmark design (libSGM) working on the Tegra X2 GPU, RT-libSGM runs more than 2× faster at a much lower energy cost.

Self-directed learning in an appropriately designed environment can help learners retain knowledge tied to experience and motivate them to learn more. For teachers, however, it is difficult to design an environment to give to learners and to give feedback that reflects respect for their independent efforts, while for learners, it is difficult to set learning objectives on their own and to construct knowledge correctly based on their own efforts. In this research, we developed a learning support system that provides a mechanism for constructing an observational learning environment using virtual space and that encourages self-directed knowledge discovery. We confirmed that this system contributes to a learner's structural understanding and its retention and to a greater desire to learn at a level comparable to that of concept map creation, another active learning method.