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.

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.

Recent character detectors have been modeled using deep neural networks and have achieved high performance in various tasks, such as text detection in natural scenes and character detection in historical documents. However, existing methods cannot achieve high detection accuracy for wooden slips because of their multi-scale character sizes and aspect ratios, high character density, and close character-to-character distance. In this study, we propose a new U-Net-based character detection and localization framework that learns character regions and boundaries between characters. The proposed method enhances the learning performance of character regions by simultaneously learning the vertical and horizontal boundaries between characters. Furthermore, by adding simple and low-cost post-processing using the learned regions of character boundaries, it is possible to more accurately detect the location of a group of characters in a close neighborhood. In this study, we construct a wooden slip dataset. Experiments demonstrated that the proposed method outperformed existing character detection methods, including state-of-the-art character detection methods for historical documents.

Far-field directivity analysis method where the FDTD method is used to calculate the near-field and then calculating far-field from the near-field has been used practically in wide variety of fields. MR/FDTD method is a simulation method derived from the FDTD method and can provide several advantages to the FDTD method. When combined with the far-field analysis, it theoretically can provide several advantages against the conventional method. In this paper, far-field analysis method that uses MR/FDTD method is introduced and its effectiveness is verified against the conventional method through numerical simulations.

This work shows a broadband, high-efficiency power amplifier (PA) monolithic microwave integrated circuit (MMIC) that uses InP-based metal-oxide-semiconductor (MOS) high-electron-mobility transistors (HEMTs) with an extended drain-side access region and broadband conjugate matching topology. Advanced device technologies, namely, double-side-doped structures, MOS gate structures, and asymmetric gate recess, were adopted, and the length of the drain-side access region was optimized to simultaneously obtain high power and efficiency. A common-source PA MMIC based on InP-based MOS-HEMTs was fabricated, and an interstage circuit was designed to maximize the S21 per unit stage in the broadband, resulting in a record-high power-added efficiency and wide bandwidth.

This paper describes a public web service called Kiite Cafe that lets users get together virtually to listen to music. When users listen to music on Kiite Cafe, their experiences are enhanced by two architectures: (i) visualization of each user's reactions, and (ii) selection of songs from users' favorite songs. These architectures enable users to feel social connection with others and the joy of introducing others to their favorite songs as if they were together listening to music in person. In addition, the architectures provide three user experiences: (1) motivation to react to played songs, (2) the opportunity to listen to a diverse range of songs, and (3) the opportunity to contribute as a curator. By analyzing the behavior logs of 2,399 Kiite Cafe users over a year, we quantitatively show that these user experiences can generate various effects (e.g., users react to a more diverse range of songs on Kiite Cafe than when listening alone). We also discuss how our proposed architectures can enrich music listening experiences with others.

As an efficient classical machine learning classifier, the Softmax regression uses cross-entropy as the loss function. Therefore, it has high accuracy in classification. However, when there is inconsistency between the distribution of training samples and test samples, the performance of traditional Softmax regression models will degrade. A transfer discriminant Softmax regression model called Transfer Discriminant Softmax Regression with Weighted MMD (TDS-WMMD) is proposed in this paper. With this method, the Weighted Maximum Mean Divergence (WMMD) is introduced into the objective function to reduce the marginal distribution and conditional distribution between domains both locally and globally, realizing the cross domain transfer of knowledge. In addition, to further improve the classification performance of the model, Linear Discriminant Analysis (LDA) is added to the label iteration refinement process to improve the class separability of the designed method by keeping the same kind of samples together and the different kinds of samples repeling each other. Finally, after conducting classification experiments on several commonly used public transfer learning datasets, the results verify that the designed method can enhance the knowledge transfer ability of the Softmax regression model, and deliver higher classification performance compared with other current transfer learning classifiers.

The ionospheric clutter in High Frequency Surface Wave Radar (HFSWR) is the reflection of electromagnetic waves from the ionosphere back to the receiver, which should be suppressed as much as possible for the primary purpose of target detection in HFSWR. However, ionospheric clutter contains vast quantities of ionospheric state information. By studying ionospheric clutter, some of the relevant ionospheric parameters can be inferred, especially during the period of typhoons, when the ionospheric state changes drastically affected by typhoon-excited gravity waves, and utilizing the time-frequency characteristics of ionospheric clutter at typhoon time, information such as the trend of electron concentration changes in the ionosphere and the direction of the typhoon can be obtained. The results of the processing of the radar data showed the effectiveness of this method.

Mission-critical monitoring services, such as finding criminals with a monitoring camera, require rapid detection of newly updated data, where suppressing delay is desirable. Taking this direction, this paper proposes a network design scheme to minimize this delay for monitoring services that consist of Internet-of-Things (IoT) devices located at terminal endpoints (TEs), databases (DB), and applications (APLs). The proposed scheme determines the allocation of DB and APLs and the selection of the server to which TE belongs. DB and APL are allocated on an optimal server from multiple servers in the network. We formulate the proposed network design scheme as an integer linear programming problem. The delay reduction effect of the proposed scheme is evaluated under two network topologies and a monitoring camera system network. In the two network topologies, the delays of the proposed scheme are 78 and 80 percent, compared to that of the conventional scheme. In the monitoring camera system network, the delay of the proposed scheme is 77 percent compared to that of the conventional scheme. These results indicate that the proposed scheme reduces the delay compared to the conventional scheme where APLs are located near TEs. The computation time of the proposed scheme is acceptable for the design phase before the service is launched. The proposed scheme can contribute to a network design that detects newly added objects quickly in the monitoring services.

We have proposed and demonstrated a device fabrication process of physically defined quantum dots utilizing electron beam lithography employing a negative-tone resist toward high-density integration of silicon quantum bits (qubits). The electrical characterization at 3.8K exhibited so-called Coulomb diamonds, which indicates successful device operation as single-electron transistors. The proposed device fabrication process will be useful due to its high compatibility with the large-scale integration process.

In this paper, we developed the latest IoT honeypots to capture IoT malware currently on the loose, analyzed IoT malware with new features such as persistent infection, developed malware removal methods to be provided to IoT device users. Furthermore, as attack behaviors using IoT devices become more diverse and sophisticated every year, we conducted research related to various factors involved in understanding the overall picture of attack behaviors from the perspective of incident responders. As the final stage of countermeasures, we also conducted research and development of IoT malware disabling technology to stop only IoT malware activities in IoT devices and IoT system disabling technology to remotely control (including stopping) IoT devices themselves.

The bin packing problem is a problem of finding an assignment of a sequence of items to a minimum number of bins, each of capacity one. An online algorithm for the bin packing problem is an algorithm that irrevocably assigns each item one by one from the head of the sequence. Gutin, Jensen, and Yeo (2006) considered a version in which all items are only of two different sizes and the online algorithm knows the two possible sizes in advance, and gave an optimal online algorithm for the case when the larger size exceeds 1/2. In this paper we provide an optimal online algorithm for some of the cases when the larger size is at most 1/2, on the basis of a framework that facilitates the design and analysis of algorithms.

This paper introduces significant improvements over the existing cryptanalysis approaches on Salsa20 and ChaCha stream ciphers. For the first time, we reduced the attack complexity on Salsa20/8 to the lowest possible margin. We introduced an attack on ChaCha7.25. It is the first attack of its type on ChaCha7.25/20. In our approach, we studied differential cryptanalysis of the Salsa20 and ChaCha stream ciphers based on a comprehensive analysis of probabilistic neutral bits (PNBs). The existing differential cryptanalysis approaches on Salsa20 and ChaCha stream ciphers first study the differential bias at specific input and output differential positions and then search for probabilistic neutral bits. However, the differential bias and the set of PNBs obtained in this method are not always the ideal combination to conduct the attack against the ciphers. The researchers have not focused on the comprehensive analysis of the probabilistic neutrality measure of all key bits concerning all possible output difference positions at all possible internal rounds of Salsa20 and ChaCha stream ciphers. Moreover, the relationship between the neutrality measure and the number of inverse quarter rounds has not been scrutinized yet. To address these study gaps, we study the differential cryptanalysis based on the comprehensive analysis of probabilistic neutral bits on the reduced-round Salsa20 and ChaCha. At first, we comprehensively analyze the neutrality measure of 256 key bits positions. Afterward, we select the output difference bit position with the best average neutrality measure and look for the corresponding input differential with the best differential bias. Considering all aspects, we present an attack on Salsa20/8 with a time complexity of 2241.62 and data complexity of 231.5, which is the best-known single bit differential attack on Salsa20/8 and then, we introduced an attack on ChaCha7.25 rounds with a time complexity of 2254.011 and data complexity of 251.81.

In recent years, the market size for internet advertising has been increasing with the expansion of the Internet. Among the internet advertising technologies, affiliate services, which are a performance-based service, use cookies to track and measure the performance of affiliates. However, for the purpose of safeguarding personal information, cookies tend to be regulated, which leads to concerns over whether normal tracking by cookies works as intended. Therefore, in this study, the recent problems from the perspectives of affiliates, affiliate service providers, and advertisers are extracted, and a framework of cookie-independent measuring engagement method using access logs is proposed and open issues are discussed for future affiliate services.

This paper addresses the novel task of detecting chorus sections in English and Japanese lyrics text. Although chorus-section detection using audio signals has been studied, whether chorus sections can be detected from text-only lyrics is an open issue. Another open issue is whether patterns of repeating lyric lines such as those appearing in chorus sections depend on language. To investigate these issues, we propose a neural-network-based model for sequence labeling. It can learn phrase repetition and linguistic features to detect chorus sections in lyrics text. It is, however, difficult to train this model since there was no dataset of lyrics with chorus-section annotations as there was no prior work on this task. We therefore generate a large amount of training data with such annotations by leveraging pairs of musical audio signals and their corresponding manually time-aligned lyrics; we first automatically detect chorus sections from the audio signals and then use their temporal positions to transfer them to the line-level chorus-section annotations for the lyrics. Experimental results show that the proposed model with the generated data contributes to detecting the chorus sections, that the model trained on Japanese lyrics can detect chorus sections surprisingly well in English lyrics, and that patterns of repeating lyric lines are language-independent.

This paper presents a hippocampal cognitive prosthesis chip designed for restoring the ability to form new long-term memories due to hippocampal system damage. The system-on-chip (SOC) consists of a 16-channel micro-power low-noise amplifier (LNA), high-pass filters, analog-digital converters (ADCs), a 16-channel spike-sorter, a generalized Laguerre-Volterra model multi-input, multi-output (GLVM-MIMO) hippocampal processor, an 8-channel neural stimulator and peripheral circuits. The proposed LNA achieved a voltage gain of 50dB, input-referred noise of 3.95µVrms, and noise efficiency factor (NEF) of 3.45 with the power consumption of 3.3µW. High-pass filters with a 300-Hz bandwidth are used to filter out the unwanted local field potential (LFP). 4 12-bit successive approximation register (SAR) ADCs with a signal-to-noise-and-distortion ratio (SNDR) of 63.37dB are designed for the digitization of the neural signals. A 16-channel spike-sorter has been integrated in the chip enabling a detection accuracy of 98.3% and a classification accuracy of 93.4% with power consumption of 19µW/ch. The MIMO hippocampal model processor predict output spatio-temporal patterns in CA1 according to the recorded input spatio-temporal patterns in CA3. The neural stimulator performs bipolar, symmetrical charge-balanced stimulation with a maximum current of 310µA, triggered by the processor output. The chip has been fabricated in 40nm standard CMOS technology, occupying a silicon area of 3mm2.

In CRYPTO 2019, Gohr first introduced the deep learning method to cryptanalysis for SPECK32/64. A differential-neural distinguisher was obtained using ResNet neural network. Zhang et al. used multiple parallel convolutional layers with different kernel sizes to capture information from multiple dimensions, thus improving the accuracy or obtaining a more round of distinguisher for SPECK32/64 and SIMON32/64. Inspired by Zhang's work, we apply the network structure to other ciphers. We not only improve the accuracy of the distinguisher, but also increase the number of rounds of the distinguisher, that is, distinguish more rounds of ciphertext and random number for DES, Chaskey and PRESENT.

This paper proposes a method in calculating the field distribution of the cross section in a transverse electromagnetic (TEM) cell based on the method of finite difference. Besides, E-field uniformity of the cross section is analyzed with the calculation results and the measured field strength. Analysis indicates that theoretical calculation via method proposed in this paper can guide the setup of E-field probes to some extent when it comes to the E-field uniformity analysis in a TEM cell.

Improvement of the on/off ratio in organic field-effect transistors through the use of pentacene and molybdenum trioxide (MoO3) layers was attempted via the preparation of a discontinuous MoO3 layer using a mesh mask. We prepared three types of devices. Device A had a conventional top-contact structure with an n-type Si wafer and a 200-nm-thick SiO2 film onto which we deposited a 70-nm-thick pentacene film and a 30-nm-thick layer of Au top electrodes. Devices B and C had a similar structure to device A but received a continuous and a discontinuous MoO3 layer, respectively. The off current in Device B was remarkably high; in contrast, the off current in Device C was reduced and dependent on the separation of the MoO3 layer. It was deduced that the high resistance of the area without MoO3 contributed to the reduced off current.

To understand the radiation mechanism of an electrically small spherical helix antenna, we develop a theory on the radiation characteristics of the antenna. An analytical model of the antenna presuming a current on the wire to be sinusoidally distributed is proposed and analyzed with the spherical wave expansion. The radiation efficiency, radiation resistance, and radiation patterns are obtained in closed-form expression. The radiation efficiency evidently varies with the surface area of the wire and the radiation resistance depends on the square of the length of the wire. The obtained result for the radiation pattern illustrates the tilt of the pattern caused by the modes asymmetric to the z-axis. The radiation efficiency formula indicates a good agreement between the simulation and measurement result. In addition, the radiation resistance of the theoretical and simulation results exhibits good agreement. Considering the effect of the feeding structure of the fabricated antenna, the radiation resistance of the analytical model can be treated as a reasonable result. The result of radiation pattern also shows good agreement between the simulation and measurement results excluding a small contribution from the feeding cable acting as a scatterer.