In the conventional single-phase PWM rectifier, the sinusoidal fluctuating current and voltage on the grid side will generate power ripple with a doubled grid frequency which leads to a secondary ripple in the DC output voltage, and the switching frequency of the conventional model predictive control strategy is not fixed. In order to solve the above two problems, a control strategy for suppressing the secondary ripple based on the three-vector fixed-frequency model predictive current control is proposed. Taking the capacitive energy storage type single-phase PWM rectifier as the research object, the principle of its active filtering is analyzed and a model predictive control strategy is proposed. Simulation and experimental results show that the proposed strategy can significantly reduce the secondary ripple of the DC output voltage, reduce the harmonic content of the input current, and achieve a constant switching frequency.

Aggregate signature schemes enable us to aggregate multiple signatures into a single short signature. One of its typical applications is sensor networks, where a large number of users and devices measure their environments, create signatures to ensure the integrity of the measurements, and transmit their signed data. However, if an invalid signature is mixed into aggregation, the aggregate signature becomes invalid, thus if an aggregate signature is invalid, it is necessary to identify the invalid signature. Furthermore, we need to deal with a situation where an invalid sensor generates invalid signatures probabilistically. In this paper, we introduce a model of aggregate signature schemes with interactive tracing functionality that captures such a situation, and define its functional and security requirements and propose aggregate signature schemes that can identify all rogue sensors. More concretely, based on the idea of Dynamic Traitor Tracing, we can trace rogue sensors dynamically and incrementally, and eventually identify all rogue sensors of generating invalid signatures even if the rogue sensors adaptively collude. In addition, the efficiency of our proposed method is also sufficiently practical.

For reliable storage services, we need a way not only to monitor the state of stored data but also to recover the original data when some data loss is discovered. To solve the problem, a novel technique called HAIL has been proposed. Unfortunately, HAIL cannot support dynamic data which is changed according to users' modification queries. There are many applications where dynamic data are used. So, we need a way to support dynamic data in cloud services to use cloud storage system for various applications. In this paper, we propose a new technique that can support the use of dynamic data in cloud storage systems. For dynamic data update, we design a new data chunk generation strategy which guarantee efficient data insertion, deletion, and modification. Our technique requires O(1) operations for each data update when existing techniques require O(n) operations where n is the size of data.

To facilitate the reuse of environmental waste heat in our society, we have developed high-efficiency flexible thermoelectric power generators (TEPGs). In this study, we investigated the thermoelectromotive force (TEMF) and output power of a prototype device with 50 pairs of Π-type structures using a homemade measurement system for flexible TEPGs in order to evaluate their characteristics along the thickness direction. The prototype device consisted of C fabrics (CAFs) used as p-type materials, NiCu fabrics (NCFs) used as n-type materials, and Ag fabrics (AGFs) used as metal electrodes. Applying a temperature difference of 5K, we obtained a TEMF of 150μV and maximum output power of 6.4pW. The obtained TEMF was smaller than that expected from the Seebeck coefficients of each fabric, which is considered to be mainly because of the influence of contact thermal resistance at the semiconductor-fabric/AGF interfaces.

The phenomenon known as social polarization, in which a social group splits into two or more groups, can cause division of the society by causing the radicalization of opinions and the spread of misinformation, is particularly significant in online communities. To develop technologies to mitigate the effects of polarization in online social networks, it is necessary to understand the mechanism driving its occurrence. There are some models of social polarization in which network structure and users' opinions change, based on the quantified opinions held by the users of online social networks. However, they are based on the interaction between users connected by online social networks. Current recommendation systems offer information from unknown users who are deemed to have similar interests. We can interpret this situation as being yielded non-local effects brought on by the network system, it is not based on local interactions between users. In this paper, based on the spectral graph theory, which can describe non-local effects in online social networks mathematically, we propose a model of polarization that user behavior and network structure change while influencing each other including non-local effects. We investigate the characteristics of the proposed model. Simultaneously, we propose an index to evaluate the degree of network polarization quantitatively, which is needed for our investigations.

This brief presents a slope analog-digital converter (ADC)-based supply voltage monitor (SVM) for biofuel-cell-powered supply-sensing systems operating in a supply voltage range of 0.18-0.35V. The proposed SVM is designed to utilize the output of energy harvester extracting power from biological reactions, realizing energy-autonomous sensor interfaces. A burst pulse generator uses a dynamic leakage suppression logic oscillator to generate a stable clock signal under the sub-threshold region for pulse counting. A slope-based voltage-to-time converter is employed to generate a pulse width proportional to the supply voltage with high linearity. The test chip of the proposed SVM is implemented in 180-nm CMOS technology with an active area of 0.018mm2. It consumes 2.1nW at 0.3V and achieves a conversion time of 117-673ms at 0.18-0.35V with a nonlinearity error of -5.5/+8.3mV, achieving an energy-efficient biosensing frontend.

Domain knowledge is useful to improve the generalization performance of learning machines. Sign constraints are a handy representation to combine domain knowledge with learning machine. In this paper, we consider constraining the signs of the weight coefficients in learning the linear support vector machine, and develop an optimization algorithm for minimizing the empirical risk under the sign constraints. The algorithm is based on the Frank-Wolfe method that also converges sublinearly and possesses a clear termination criterion. We show that each iteration of the Frank-Wolfe also requires O(nd+d2) computational cost. Furthermore, we derive the explicit expression for the minimal iteration number to ensure an ε-accurate solution by analyzing the curvature of the objective function. Finally, we empirically demonstrate that the sign constraints are a promising technique when similarities to the training examples compose the feature vector.

Graphene has been expected as an alternative material for copper interconnects in which resistance increases and reliability deteriorates in nanoscale. While the principle advantages are verified by simulations and experiments, they have not been put into practical use due to the immaturity of the manufacturing process leading to mass production. On the other hand, recent steady progress in the fabrication process has increased the possibility of practical application. In this paper, I will review the recent advances and the latest prospects for conductor applications of graphene centered on interconnects. The possibility of further application utilizing the unique characteristics of graphene is discussed.

Stance prediction on social media aims to infer the stances of users towards a specific topic or event, which are not expressed explicitly. It is of great significance for public opinion analysis to extract and determine users' stances using user-generated content on social media. Existing research makes use of various signals, ranging from text content to online network connections of users on these platforms. However, it lacks joint modeling of the heterogeneous information for stance prediction. In this paper, we propose a self-supervised heterogeneous graph contrastive learning framework for stance prediction in online debate forums. Firstly, we perform data augmentation on the original heterogeneous information network to generate an augmented view. The original view and augmented view are learned from a meta-path based graph encoder respectively. Then, the contrastive learning among the two views is conducted to obtain high-quality representations of users and issues. Finally, the stance prediction is accomplished by matrix factorization between users and issues. The experimental results on an online debate forum dataset show that our model outperforms other competitive baseline methods significantly.

In this paper, we propose a non-orthogonal multiple access with adaptive resource allocation. The proposed non-orthogonal multiple access assigns multiple frequency resources for each device to send packets. Even if the number of devices is more than that of the available frequency resources, the proposed non-orthogonal access allows all the devices to transmit their packets simultaneously for high capacity massive machine-type communications (mMTC). Furthermore, this paper proposes adaptive resource allocation algorithms based on factor graphs that adaptively allocate the frequency resources to the devices for improvement of the transmission performances. This paper proposes two allocation algorithms for the proposed non-orthogonal multiple access. This paper shows that the proposed non-orthogonal multiple access achieves superior transmission performance when the number of the devices is 50% greater than the amount of the resource, i.e., the overloading ratio of 1.5, even without the adaptive resource allocation. The adaptive resource allocation enables the proposed non-orthogonal access to attain a gain of about 5dB at the BER of 10-4.

A detailed investigation of DC and RF performance of AlGaN/GaN HEMT on 3C-SiC/low resistive silicon (LR-Si) substrate by introducing a thick GaN layer is reported in this paper. The hetero-epitaxial growth is achieved by metal organic chemical vapor deposition (MOCVD) on a commercially prepared 6-inch LR-Si substrate via a 3C-SiC intermediate layer. The reported HEMT exhibited very low RF loss and thermally stable amplifier characteristics with the introduction of a thick GaN layer. The temperature-dependent small-signal and large-signal characteristics verified the effectiveness of the thick GaN layer on LR-Si, especially in reduction of RF loss even at high temperatures. In summary, a high potential of the reported device is confirmed for microwave applications.

In this research, the effect of Ar/N2-plasma sputtering gas pressure on the LaBxNy tunnel and block layer was investigated for pentacene-based floating-gate memory with an amorphous rubrene (α-rubrene) passivation layer. The influence of α-rubrene passivation layer for memory characteristic was examined. The pentacene-based metal/insulator/metal/insulator/semiconductor (MIMIS) diode and organic field-effect transistor (OFET) were fabricated utilizing N-doped LaB6 metal layer and LaBxNy insulator with α-rubrene passivation layer at annealing temperature of 200°C. In the case of MIMIS diode, the leakage current density and the equivalent oxide thickness (EOT) were decreased from 1.2×10-2 A/cm2 to 1.1×10-7 A/cm2 and 3.5 nm to 3.1 nm, respectively, by decreasing the sputtering gas pressure from 0.47 Pa to 0.19 Pa. In the case of floating-gate type OFET with α-rubrene passivation layer, the larger memory window of 0.68 V was obtained with saturation mobility of 2.2×10-2 cm2/(V·s) and subthreshold swing of 199 mV/dec compared to the device without α-rubrene passivation layer.

We have investigated the electromotive force (EMF) of a composite sample consisting of a Π-type thermoelectric power generation structure with a pair of n- and p-type Si wafers and piezoelectric devices in order to collect electricity from vibration energy and thermal energy, simultaneously. The observed EMF was obtained by superimposing the oscillating EMF of vibration energy on the constant EMF of thermal energy. Therefore, we have improved the composite sample with diodes for rectifying the oscillating EMF. As a result, the full-wave rectification and the preservation of EMF amplitude were realized. From the frequency dependence, it was found that the dielectric loss of the piezoelectric device influences the amplitude and the time delay in the EMF.

A neural conversational model (NCM) based on an encoder-decoder recurrent neural network (RNN) with an attention mechanism learns different sequence-to-sequence mappings from what neural machine translation (NMT) learns even when based on the same technique. In the NCM, we confirmed that target-word-to-source-word mappings captured by the attention mechanism are not as clear and stationary as those for NMT. Considering that vector norms indicate a magnitude of information in the processing, we analyzed the inner workings of an encoder-decoder GRU-based NCM focusing on the norms of word embedding vectors and hidden vectors. First, we conducted correlation analyses on the norms of word embedding vectors with frequencies in the training set and with conditional entropies of a bi-gram language model to understand what is correlated with the norms in the encoder and decoder. Second, we conducted correlation analyses on norms of change in the hidden vector of the recurrent layer with their input vectors for the encoder and decoder, respectively. These analyses were done to understand how the magnitude of information propagates through the network. The analytical results suggested that the norms of the word embedding vectors are associated with their semantic information in the encoder, while those are associated with the predictability as a language model in the decoder. The analytical results further revealed how the norms propagate through the recurrent layer in the encoder and decoder.

There are continuous and strong demands for the DC-DC converter to reduce the size of passive components and increase the system power density. Advances in CMOS processes and GaN FETs enabled the switching frequency of DC-DC converters to be beyond 10MHz. The advancements of 3-D integrated magnetics will further reduce the footprint. In this paper, the overview of beyond-10MHz DC-DC converters will be provided first, and our recent achievements are introduced focusing on 3D-integration of Fe-based metal composite magnetic core inductor, and GaN FET control designs.

The Graph Coloring Problem (GCP) is a fundamental combinatorial optimization problem that has many practical applications. Degree of SATURation (DSATUR) and Recursive Largest First (RLF) are well known as typical solution construction algorithms for GCP. It is necessary to update the vertex degree in the subgraph induced by uncolored vertices when selecting vertices to be colored in both DSATUR and RLF. There is an issue that the higher the edge density of a given graph, the longer the processing time. The purposes of this paper are to propose a degree updating method called Adaptive Degree Updating (ADU for short) that improves the issue, and to evaluate the effectiveness of ADU for DSATUR and RLF on DIMACS benchmark graphs as well as random graphs having a wide range of sizes and densities. Experimental results show that the construction algorithms with ADU are faster than the conventional algorithms for many graphs and that the ADU method yields significant speed-ups relative to the conventional algorithms, especially in the case of large graphs with higher edge density.

We propose a penalty-based and constraint Bayesian optimization methods with an agent-based supply-chain (SC) simulator as a new Monte Carlo optimization approach for multi-echelon inventory management to improve key performance indicators such as inventory cost and sales opportunity loss. First, we formulate the multi-echelon inventory problem and introduce an agent-based SC simulator architecture for the optimization. Second, we define the optimization framework for the formulation. Finally, we discuss the evaluation of the effectiveness of the proposed methods by benchmarking it against the most commonly used genetic algorithm (GA) in simulation-based inventory optimization. Our results indicate that the constraint Bayesian optimization can minimize SC inventory cost with lower sales opportunity loss rates and converge to the optimal solution 22 times faster than GA in the best case.

A Mach-Zehnder optical modulator with the tunable multimode interference coupler was fabricated using Ti-diffused LiNbO3. The modulation extinction ratio could be voltage controlled to maximize up to 50 dB by tuning the coupler. Optical single-sideband modulation was also achieved with a sideband suppression ratio of more than 30 dB.

This paper reports on the ambipolar conduction for the λ-Deoxyribonucleic Acid (DNA) field effect transistor (FET) with 450, 400 and 250 base pair experimentally and theoretically. It was found that the drain current of the p-type DNA/Si FET increased as the ratio of the guanine-cytosine (GC) pair increased and that of the n-type DNA/Si FET decreased as the ratio of the adenine-thymine (AT) pair decreased, and the ratio of the GC pair and AT pair was controlled by the total number of the base pair. In addition, it was found that the hole conduction mechanism of the 400 bp DNA/Si FET was polaron hopping and its activation energy was 0.13eV. By considering the electron affinity of the adenine, thymine, guanine, and cytosine, the ambipolar characteristics of the DNA/Si FET was understood. The holes are injected to the guanine base for the negative gate voltage, and the electrons are injected to the adenine, thymine, and cytosine for the positive gate voltage.

Time-of-flight (TOF) range imaging is a promising technology for various applications such as touchless control, augmented reality interface, and automotive. The TOF range imagers are classified into two methods: direct TOF with single photo avalanche diodes and indirect TOF with lock-in pixels. The indirect TOF range imagers have advantages in terms of a high spatial resolution and high depth precision because their pixels are simple and can handle many photons at one time. This paper reviews and discusses principal lock-in pixels reported both in the past and present, including circuit-based and charge-modulator-based lock-in pixels. In addition, key technologies that include enhancing sensitivity and background suppression techniques are also discussed.