Drones have been attractive for many kinds of industries, but limited power supply from batteries has impeded drones from being operated for longer hours. Microwave power transmission (MPT) is one of the most prospective technologies to release them from the limitation. Since, among several types of drones, micro-drone has shorter available flight time, it is reasonable to provide micro-drone with wireless charging access with an MPT system. However, there is no suitable rectenna for micro-drone charging applications in preceding studies. In this paper, an MPT system for micro-drone was proposed at C-band where a lightweight and compact rectenna array with 20-W class output power was developed. Under illumination of a flat-top beam with 203 mW/cm2 of power density, a 16-element rectenna array was measured. The 16-element rectenna was formed with the aid of a honeycomb substrate for lightness and GaAs Schottky barrier diodes for high output. It was 37.5 g in weight and 146.4 mm by 146.4 mm in size. It output 27.0 W of dc power at 19.0 V at 5.8 GHz when radio frequency power of 280 W was generated by the injection-locked magnetron and 134 W was transmitted from the transmitting phased array. The power-to-weight ratio was 0.72W/g. The power conversion efficiency was 61.9%. These numbers outperformed the rectennas in the preceding studies and are suitable for an MPT system to micro-drone.

Low-temperature deposition of Y2O3 at 80°C is studied using an yttrium precursor of tris(butylcyclopentadienyl)yttrium (Y(BuCp)3) and plasma exited humidified argon oxidizer. The deposition is demonstrated using an atomic-layer-deposition sequence; the Y(BuCp)3 and the oxidizing gases are time separately introduced to the reaction chamber and these injections are repeated. To determine the gas introduction conditions, surface reactions of Y(BuCp)3 adsorption and its oxidization are observed by an in-situ IR absorption spectroscopy. The deposited film is confirmed as fully oxidized Y2O3 by X-ray photoelectron spectroscopy. The present deposition is applicable for the deposition of Y2O3 film on flexible polyethylene terephthalate films.

In this research, we investigated the effect of Hf inter layer and chemical oxide on Si(100) substrate on the ferroelectric undoped HfO2 deposition. In case with 1 nm-thick Hf inter layer, equivalent oxide thickness (EOT) was decreased from 6.0 to 4.8 nm for 10 nm-thick HfO2 with decreasing annealing temperature. In case with 0.5 nm-thick chemical oxide, EOT was decreased from 3.9 to 3.6 nm in MFS diodes for 5 nm-thick HfO2. The MFSFET was fabricated with 10 nm-thick HfO2 utilizing Hf inter layer. The subthreshold swing was improved from 240 mV/dec. to 120 mV/dec. and saturation mobility was increased from 70 cm2/(Vs) to 140 cm2/(Vs) by inserting Hf inter layer.

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.

The low temperature deposition of AlN at 160 °C is examined by using trimethyl aluminum (TMA) and NH radicals from plasma excited Ar diluted ammonia. For the deposition, a plasma tube separated from the reaction chamber is used to introduce the neutral NH radicals on the growing surface without the direct impacts of high-speed species and UV photons, which might be effective in suppressing the plasma damage to the sample surfaces. To maximize the NH radical generation, the NH3 and Ar mixing ratio is optimized by plasma optical emission spectroscopy. To determine the saturated condition of TMA and NH radical irradiations, an in-situ surface observation of IR absorption spectroscopy (IRAS) with a multiple internal reflection geometry is utilized. The low temperature AlN deposition is performed with the TMA and NH radical exposures whose conditions are determined by the IRAS experiment. The spectroscopic ellipsometry indicates the all-round surface deposition in which the growth per cycles measured from front and backside surfaces of the Si sample are of the same range from 0.39∼0.41nm/cycle. It is confirmed that the deposited film contains impurities of C, O, N although we discuss the method to decrease them. X-ray diffraction suggests the AlN polycrystal deposition with crystal phases of AlN (100), (002) and (101). From the saturation curves of TMA adsorption and its nitridation, their chemical reactions are discussed in this paper. In the present paper, we discuss the possibility of the low temperature AlN deposition.

We fabricated Fe-silicide nanodots (NDs) on an ultrathin SiO2 layer and evaluated changes in electron transport properties with and without magnetic field application. High-density NDs with an areal density as high as ∼1011cm-2 were formed on thermally grown SiO2 by exposing ultrathin Fe/Si-NDs structures to a remote H2 plasma without external heating. In electron transport properties related to current-time characteristics for a diode with Fe electrode and charging energy to NDs, clear changes in current levels through NDs and electron injection modulation of NDs depending on intensity of magnetic fields were observed.

A static timing analysis (STA) tool for a 28nm atom-switch FPGA (AS-FPGA) is introduced to validate the signal delay of an application circuit before implementation. High accuracy of the STA tool is confirmed by implementing a practical application circuit on the 28nm AS-FPGA. Moreover, dramatic improvement of delay and power is demonstrated in comparison with a previous 40nm AS-FPGA.

When BJTs are irradiated by gamma rays, interface trapped charges and positive oxide trapped charges are formed by ionization at the Si-SiO2 interface and SiO2 regions, respectively. These trapped charges affect the movement of carriers depending on the type of BJT. This paper presents experimental results regarding operating characteristics of gamma irradiated pnp Si BJTs.

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 light field (LF), which is represented as a set of dense, multi-view images, has been used in various 3D applications. To make LF acquisition more efficient, researchers have investigated compressive sensing methods by incorporating certain coding functionalities into a camera. In this paper, we focus on a challenging case called snapshot compressive LF imaging, in which an entire LF is reconstructed from only a single acquired image. To embed a large amount of LF information in a single image, we consider two promising methods based on rapid optical control during a single exposure: time-multiplexed coded aperture (TMCA) and coded focal stack (CFS), which were proposed individually in previous works. Both TMCA and CFS can be interpreted in a unified manner as extensions of the coded aperture (CA) and focal stack (FS) methods, respectively. By developing a unified algorithm pipeline for TMCA and CFS, based on deep neural networks, we evaluated their performance with respect to other possible imaging methods. We found that both TMCA and CFS can achieve better reconstruction quality than the other snapshot methods, and they also perform reasonably well compared to methods using multiple acquired images. To our knowledge, we are the first to present an overall discussion of TMCA and CFS and to compare and validate their effectiveness in the context of compressive LF imaging.

Geometric partitioning mode (GPM) is a new inter prediction tool adopted in versatile video coding (VVC), which is the latest video coding of international standard developed by joint video expert team in 2020. Different from the regular inter prediction performed on rectangular blocks, GPM separates a coding block into two regions by the pre-defined 64 types of straight lines, generates inter predicted samples for each separated region, and then blends them to obtain the final inter predicted samples. With this feature, GPM improves the prediction accuracy at the boundary between the foreground and background with different motions. However, GPM has room to further improve the prediction accuracy if the final predicted samples can be generated using not only inter prediction but also intra prediction. In this paper, we propose a GPM with inter and intra prediction to achieve further enhanced compression capability beyond VVC. To maximize the coding performance of the proposed method, we also propose the restriction of the applicable intra prediction mode number and the prohibition of applying the intra prediction to both GPM-separated regions. The experimental results show that the proposed method improves the coding performance gain by the conventional GPM method of VVC by 1.3 times, and provides an additional coding performance gain of 1% bitrate savings in one of the coding structures for low-latency video transmission where the conventional GPM method cannot be utilized.

It is important to consider curvature properties around the control points to produce natural-looking results in the vector illustration. C2 interpolating splines satisfy point interpolation with local support. Unfortunately, they cannot control the sharpness of the segment because it utilizes trigonometric function as blending function that has no degree of freedom. In this paper, we alternate the definition of C2 interpolating splines in both interpolation curve and blending function. For the interpolation curve, we adopt a rational Bézier curve that enables the user to tune the shape of curve around the control point. For the blending function, we generalize the weighting scheme of C2 interpolating splines and replace the trigonometric weight to our novel hyperbolic blending function. By extending this basic definition, we can also handle exact non-C2 features, such as cusps and fillets, without losing generality. In our experiment, we provide both quantitative and qualitative comparisons to existing parametric curve models and discuss the difference among them.

Crowdedness of buses is playing an increasingly important role in the disease control of COVID-19. The lack of a practical approach to sensing the crowdedness of buses is a major problem. This paper proposes a bus crowdedness sensing system which exploits deep learning-based object detection to count the numbers of passengers getting on and off a bus and thus estimate the crowdedness of buses in real time. In our prototype system, we combine YOLOv5s object detection model with Kalman Filter object tracking algorithm to implement a sensing algorithm running on a Jetson nano-based vehicular device mounted on a bus. By using the driving recorder video data taken from real bus, we experimentally evaluate the performance of the proposed sensing system to verify that our proposed system system improves counting accuracy and achieves real-time processing at the Jetson Nano platform.

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.

In this paper, we propose a scheme to strengthen network-based moving target defense with disposable identifiers. The main idea is to change disposable identifiers for each packet to maximize unpredictability with large hopping space and substantially high hopping frequency. It allows network-based moving target defense to defeat active scanning, passive scanning, and passive host profiling attacks. Experimental results show that the proposed scheme changes disposable identifiers for each packet while requiring low overhead.

In video-text retrieval task, mainstream framework consists of three parts: video encoder, text encoder and similarity calculation. MMT (Multi-modal Transformer) achieves remarkable performance for this task, however, it faces the problem of insufficient training dataset. In this paper, an efficient multimodal aggregation network for video-text retrieval is proposed. Different from the prior work using MMT to fuse video features, the NetVLAD is introduced in the proposed network. It has fewer parameters and is feasible for training with small datasets. In addition, since the function of CLIP (Contrastive Language-Image Pre-training) can be considered as learning language models from visual supervision, it is introduced as text encoder in the proposed network to avoid overfitting. Meanwhile, in order to make full use of the pre-training model, a two-step training scheme is designed. Experiments show that the proposed model achieves competitive results compared with the latest work.

This letter focuses on the cross-corpus speech emotion recognition (SER) task, in which the training and testing speech signals in cross-corpus SER belong to different speech corpora. Existing algorithms are incapable of effectively extracting common sentiment information between different corpora to facilitate knowledge transfer. To address this challenging problem, a novel convolutional auto-encoder and adversarial domain adaptation (CAEADA) framework for cross-corpus SER is proposed. The framework first constructs a one-dimensional convolutional auto-encoder (1D-CAE) for feature processing, which can explore the correlation among adjacent one-dimensional statistic features and the feature representation can be enhanced by the architecture based on encoder-decoder-style. Subsequently the adversarial domain adaptation (ADA) module alleviates the feature distributions discrepancy between the source and target domains by confusing domain discriminator, and specifically employs maximum mean discrepancy (MMD) to better accomplish feature transformation. To evaluate the proposed CAEADA, extensive experiments were conducted on EmoDB, eNTERFACE, and CASIA speech corpora, and the results show that the proposed method outperformed other approaches.

Inter-channel correlation is one of the redundancy which need to be eliminated in video coding. In the latest video coding standard H.266/VVC, the DM (Direct Mode) and CCLM (Cross-component Linear Model) modes have been introduced to reduce the similarity between luminance and chroma. However, inter-channel correlation is still observed. In this paper, a new inter-channel prediction algorithm is proposed, which utilizes coloring principle to predict chroma pixels. From the coloring perspective, for most natural content video frames, the three components Y, U and V always demonstrate similar coloring pattern. Therefore, the U and V components can be predicted using the coloring pattern of the Y component. In the proposed algorithm, correlation coefficients are obtained in a lightweight way to describe the coloring relationship between current pixel and reference pixel in Y component, and used to predict chroma pixels. The optimal position for the reference samples is also designed. Base on the selected position of the reference samples, two new chroma prediction modes are defined. Experiment results show that, compared with VTM 12.1, the proposed algorithm has an average of -0.92% and -0.96% BD-rate improvement for U and V components, for All Intra (AI) configurations. At the same time, the increased encoding time and decoding time can be ignored.

The purpose of graph embedding is to learn a lower-dimensional embedding function for graph data. Existing methods usually rely on maximum likelihood estimation (MLE), and often learn an embedding function through conditional mean estimation (CME). However, MLE is well-known to be vulnerable to the contamination of outliers. Furthermore, CME might restrict the applicability of the graph embedding methods to a limited range of graph data. To cope with these problems, this paper proposes a novel method for graph embedding called the robust ratio graph embedding (RRGE). RRGE is based on the ratio estimation between the conditional and marginal probability distributions of link weights given data vectors, and would be applicable to a wider-range of graph data than CME-based methods. Moreover, to achieve outlier-robust estimation, the ratio is estimated with the γ-cross entropy, which is a robust alternative to the standard cross entropy. Numerical experiments on artificial data show that RRGE is robust against outliers and performs well even when CME-based methods do not work at all. Finally, the performance of the proposed method is demonstrated on realworld datasets using neural networks.

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.