Capacitive coupling of line coded and DC-balanced digital signals is often used to eliminate steady bias current flow between the systems or components in various communication systems. A multi-layer ceramic chip capacitor is promising for the capacitor of very broadband signal coupling because of its high frequency characteristics expected from the downsizing of the chip recent years. The lower limit of the coupling bandwidth is determined by the capacitance while the higher limit is affected by the parasitic inductance associated with the chip structure. In this paper, we investigate the coupling characteristics up to millimeter wave frequencies by the measurement and simulations. A phenomenon has been found in which the change in the current distribution in the chip structure occur at high frequencies and the coupling characteristics are improved compared to the prediction based on the conventional equivalent circuit model. A new equivalent circuit model of chip capacitor that can express the effect of the improvement has been proposed.

This paper proposes a voice conversion (VC) method based on a model that links linguistic and acoustic representations via latent phonological distinctive features. Our method, called speech chain VC, is inspired by the concept of the speech chain, where speech communication consists of a chain of events linking the speaker's brain with the listener's brain. We assume that speaker identity information, which appears in the acoustic level, is embedded in two steps — where phonological information is encoded into articulatory movements (linguistic to physiological) and where articulatory movements generate sound waves (physiological to acoustic). Speech chain VC represents these event links by using an adaptive restricted Boltzmann machine (ARBM) introducing phoneme labels and acoustic features as two classes of visible units and latent phonological distinctive features associated with articulatory movements as hidden units. Subjective evaluation experiments showed that intelligibility of the converted speech significantly improved compared with the conventional ARBM-based method. The speaker-identity conversion quality of the proposed method was comparable to that of a Gaussian mixture model (GMM)-based method. Analyses on the representations of the hidden layer of the speech chain VC model supported that some of the hidden units actually correspond to phonological distinctive features. Final part of this paper proposes approaches to achieve one-shot VC by using the speech chain VC model. Subjective evaluation experiments showed that when a target speaker is the same gender as a source speaker, the proposed methods can achieve one-shot VC based on each single source and target speaker's utterance.

For low-density parity-check (LDPC) codes, the penalized decoding method based on the alternating direction method of multipliers (ADMM) can improve the decoding performance at low signal-to-noise ratios and also has low decoding complexity. There are three effective methods that could increase the ADMM penalized decoding speed, which are reducing the number of Euclidean projections in ADMM penalized decoding, designing an effective penalty function and selecting an appropriate layered scheduling strategy for message transmission. In order to further increase the ADMM penalized decoding speed, through reducing the number of Euclidean projections and using the vertical layered scheduling strategy, this paper designs a fast converging ADMM penalized decoding method based on the improved penalty function. Simulation results show that the proposed method not only improves the decoding performance but also reduces the average number of iterations and the average decoding time.

Data aggregation trees in wireless sensor networks (WSNs) are being used for gathering data for various purposes. Especially for the trees within buildings or civil structures, the total amount of energy consumption in a tree must be reduced to save energy. Therefore, the minimum energy-cost aggregation tree (MECAT) and MECAT with relay nodes (MECAT_RN) problems are being discussed to reduce energy consumption in data aggregation trees in WSNs. This paper proposes the tree node switching algorithm (TNSA) that improves on the previous algorithms for the MECAT and MECAT_RN problems in terms of energy efficiency. TNSA repeatedly switches nodes in a tree to reduce the number of packets sent in the tree. Packets are reduced by improving the accommodation efficiency of each packet, in which multiple sensor reports are accommodated. As a result of applying TNSA to MECATs and MECAT-RNs, energy consumption can be reduced significantly with a small burden.

Recently, intelligent heating, next generation microwave ovens that achieve uniform heating and spot heating using solid-state devices, has been actively studied. There are two types of microwave generators using solid-state devices. Since compactness is indispensable to accommodate in a limited space, the miniaturized oscillator type was selected. The authors proposed an imbalanced coupling resonator, a resonator-less feedback circuit, a high power frequency variable resonator, and injection-locked phase control in order to achieve high performance of the oscillator type microwave generator. In addition, we confirmed that the oscillator type can be used as the microwave generator for intelligent heating using a Wilkinson combiner. As a result, it was demonstrated that the oscillator type microwave generator, realized the same high efficiency (67%) as the amplifier type, and found the possibility of variable frequency (2.4 to 2.5GHz) and variable phase, and can be used as the microwave generator for intelligent heating.

This paper presents analysis results of the intermodulation distortion (IMD) components compensation conditions for dual-band feed-forward power amplifier (FFPA) when inputting dual-band signals simultaneously. The signal cancellation loop and distortion cancellation loop of the dual-band FFPA have frequency selective adjustment paths which consist of filter and vector regulator. The filter selects the desired frequency component and suppresses the undesired frequency component in the desired frequency selective adjustment path. The vector regulators repeatedly adjust the amplitude and phase values of the composite components for the desired and suppressed undesired frequency components. In this configuration, the cancellation levels of the signal cancellation loop and distortion cancellation loop are depending on the amplitude and phase errors of the vector regulator. The analysis results show that the amplitude and phase errors of the desired frequency component almost become independent that of the undesired frequency component in a weak non-linearity condition, when the isolation between the desired band and the undesired band given by the filter is more than 40 dB. The amplitude errors of the desired frequency component are dependent on that of the undesired frequency component in a strong non-linear conditions when the isolation level sets as above. A 1-W-class signal cancellation loop and 20-W-class FFPA are fabricated for 1.7-GHz and 2.1-GHz bands simultaneous operation. The experimental results show that the analysis results are suitable in the experimental conditions. From these investigations, the analysis results can provide a commercially available dual-band FFPA. To our best knowledge, this is first analysis results for the dual-band FFPA.

Speech enhancement has been proposed to reduce the impulsive noise whose frequency characteristic is wideband. On the other hand, it is challenging to reduce the ringing sound, which is narrowband in impulsive noise. Therefore, we propose the modeling of the ringing sound and its estimation by a linear predictor (LP). However, it is difficult to estimate the ringing sound only in noisy speech due to the auto-correlation property of speech. The proposed system adopts the 4th order moment-based adaptive algorithm by noticing the difference between the 4th order statistics of speech and impulsive noise. The brief analysis and simulation results show that the proposed system has the potential to reduce ringing sound while keeping the quality of enhanced speech.

In this study, the transient characteristics on the super-steep subthreshold slope (SS) of a PN-body tied (PNBT) silicon-on-insulator field-effect transistor (SOI-FET) were investigated using technology computer-aided design and pulse measurements. Carrier charging effects were observed on the super-steep SS PNBT SOI-FET. It was found that the turn-on delay time decreased to nearly zero when the gate overdrive-voltage was set to 0.1-0.15 V. Additionally, optimizing the gate width improved the turn-on delay. This has positive implications for the low speed problems of this device. However, long-term leakage current flows on turn-off. The carrier lifetime affects the leakage current, and the device parameters must be optimized to realize both a high on/off ratio and high-speed operation.

Introduction of Fully Convolutional Networks (FCNs) has made record progress in salient object detection models. However, in order to retain the input resolutions, deconvolutional networks with unpooling are applied on top of FCNs. This will cause the increase of the computation and network model size in segmentation task. In addition, most deep learning based methods always discard effective saliency prior knowledge completely, which are shown effective. Therefore, an efficient salient object detection method based on deep learning is proposed in our work. In this model, dilated convolutions are exploited in the networks to produce the output with high resolution without pooling and adding deconvolutional networks. In this way, the parameters and depth of the network are decreased sharply compared with the traditional FCNs. Furthermore, manifold ranking model is explored for the saliency refinement to keep the spatial consistency and contour preserving. Experimental results verify that performance of our method is superior with other state-of-art methods. Meanwhile, the proposed model occupies the less model size and fastest processing speed, which is more suitable for the wearable processing systems.

To conduct empirical research on industry software development, it is necessary to obtain data of real software projects from industry. However, only few such industry data sets are publicly available; and unfortunately, most of them are very old. In addition, most of today's software companies cannot make their data open, because software development involves many stakeholders, and thus, its data confidentiality must be strongly preserved. To that end, this study proposes a method for artificially generating a “mimic” software project data set, whose characteristics (such as average, standard deviation and correlation coefficients) are very similar to a given confidential data set. Instead of using the original (confidential) data set, researchers are expected to use the mimic data set to produce similar results as the original data set. The proposed method uses the Box-Muller transform for generating normally distributed random numbers; and exponential transformation and number reordering for data mimicry. To evaluate the efficacy of the proposed method, effort estimation is considered as potential application domain for employing mimic data. Estimation models are built from 8 reference data sets and their concerning mimic data. Our experiments confirmed that models built from mimic data sets show similar effort estimation performance as the models built from original data sets, which indicate the capability of the proposed method in generating representative samples.

Neural networks have received considerable attention in sentence similarity measuring systems due to their efficiency in dealing with semantic composition. However, existing neural network methods are not sufficiently effective in capturing the most significant semantic information buried in an input. To address this problem, a novel weighted-pooling attention layer is proposed to retain the most remarkable attention vector. It has already been established that long short-term memory and a convolution neural network have a strong ability to accumulate enriched patterns of whole sentence semantic representation. First, a sentence representation is generated by employing a siamese structure based on bidirectional long short-term memory and a convolutional neural network. Subsequently, a weighted-pooling attention layer is applied to obtain an attention vector. Finally, the attention vector pair information is leveraged to calculate the score of sentence similarity. An amalgamation of both, bidirectional long short-term memory and a convolutional neural network has resulted in a model that enhances information extracting and learning capacity. Investigations show that the proposed method outperforms the state-of-the-art approaches to datasets for two tasks, namely semantic relatedness and Microsoft research paraphrase identification. The new model improves the learning capability and also boosts the similarity accuracy as well.

Hum noise such as power line interference is one of the critical problems in the biomedical signal acquisition. Various techniques have been proposed to suppress power line interference. However, some of the techniques require more components and power consumption. The notch depth in the conventional N-path notch filter circuits needs a higher number of paths and switches off-resistance. It makes the conventional N-path notch filter less of efficiency to suppress hum noise. This work proposed the new N-path notch filter to hum noise suppression in biomedical signal acquisition. The new N-path notch filter achieved notch depth above 40dB with sampling frequency 50Hz and 60Hz. Although the proposed circuits use less number of path and switches off-resistance. The proposed circuit has been verified using artificial ECG signal contaminated by hum noise at frequency 50Hz and 60Hz. The output of N-path notch filter achieved a noise-free signal even if the sampling frequency changes.

Many studies of deep neural networks have reported inference accelerators for improved energy efficiency. We propose methods for further improving energy efficiency while maintaining recognition accuracy, which were developed by the co-design of a filter-by-filter quantization scheme with variable bit precision and a hardware architecture that fully supports it. Filter-wise quantization reduces the average bit precision of weights, so execution times and energy consumption for inference are reduced in proportion to the total number of computations multiplied by the average bit precision of weights. The hardware utilization is also improved by a bit-parallel architecture suitable for granularly quantized bit precision of weights. We implement the proposed architecture on an FPGA and demonstrate that the execution cycles are reduced to 1/5.3 for ResNet-50 on ImageNet in comparison with a conventional method, while maintaining recognition accuracy.

Radio frequency energy transfer (RET) technology has been introduced as a promising energy harvesting (EH) method to supply power in both wireless communication (WLC) and power-line communication (PLC) systems. However, current RET modified MAC (medium access control) protocols have been proposed only for WLC systems. Due to the difference in the MAC standard between WLC and PLC systems, these protocols are not suitable for PLC systems. Therefore, how to utilize RET technology to modify the MAC protocol of PLC systems (i.e., IEEE 1901), which can use the radio frequency signal to provide the transmission power and the PLC medium to finish the data transmission, i.e., realizing the ‘cooperative communication’ remains a challenge. To resolve this problem, we propose a RET modified MAC protocol for PLC systems (RET-PLC MAC). Firstly, we improve the standard PLC frame sequence by adding consultation and confirmation frames, so that the station can obtain suitable harvested energy, once it occupied the PLC medium, and the PLC system can be operated in an on-demand and self-sustainable manner. On this basis, we present the working principle of RET-PLC MAC. Then, we establish an analytical model to allow mathematical verification of RET-PLC MAC. A 2-dimension discrete Markov chain model is employed to derive the numerical analysis results of RET-PLC MAC. The impacts of buffer size, traffic rate, deferral counter process of 1901, heterogeneous environment and quality of information (QoI) are comprehensively considered in the modeling process. Moreover, we deduce the optimal results of system throughput and expected QoI. Through extensive simulations, we show the performance of RET-PLC MAC under different system parameters, and verify the corresponding analytical model. Our work provides insights into realizing cooperative communication at PLC's MAC layer.

This paper introduces the maximization version of the k-path vertex cover problem, called the MAXIMUM K-PATH VERTEX COVER problem (MaxPkVC for short): A path consisting of k vertices, i.e., a path of length k-1 is called a k-path. If a k-path Pk includes a vertex v in a vertex set S, then we say that v or S covers Pk. Given a graph G=(V, E) and an integer s, the goal of MaxPkVC is to find a vertex subset S⊆V of at most s vertices such that the number of k-paths covered by S is maximized. The problem MaxPkVC is generally NP-hard. In this paper we consider the tractability/intractability of MaxPkVC on subclasses of graphs. We prove that MaxP3VC remains NP-hard even for split graphs. Furthermore, if the input graph is restricted to graphs with constant bounded treewidth, then MaxP3VC can be solved in polynomial time.

Direction of arrival (DOA) estimation of wireless signals has a long history but is still being investigated to improve the estimation accuracy. Non-linear algorithms such as compressed sensing are now applied to DOA estimation and achieve very high performance. If the large computational loads of compressed sensing algorithms are acceptable, it may be possible to apply a deep neural network (DNN) to DOA estimation. In this paper, we verify on-grid DOA estimation capability of the DNN under a simple estimation situation and discuss the effect of training data on DNN design. Simulations show that SNR of the training data strongly affects the performance and that the random SNR data is suitable for configuring the general-purpose DNN. The obtained DNN provides reasonably high performance, and it is shown that the DNN trained using the training data restricted to close DOA situations provides very high performance for the close DOA cases.

This paper presents a method for many-to-one voice conversion using phonetic posteriorgrams (PPGs) based on an adversarial training of deep neural networks (DNNs). A conventional method for many-to-one VC can learn a mapping function from input acoustic features to target acoustic features through separately trained DNN-based speech recognition and synthesis models. However, 1) the differences among speakers observed in PPGs and 2) an over-smoothing effect of generated acoustic features degrade the converted speech quality. Our method performs a domain-adversarial training of the recognition model for reducing the PPG differences. In addition, it incorporates a generative adversarial network into the training of the synthesis model for alleviating the over-smoothing effect. Unlike the conventional method, ours jointly trains the recognition and synthesis models so that they are optimized for many-to-one VC. Experimental evaluation demonstrates that the proposed method significantly improves the converted speech quality compared with conventional VC methods.

Fresh Tea Shoot Maturity Estimation (FTSME) is the basement of automatic tea picking technique, determines whether the shoot can be picked. Unfortunately, the ambiguous information among single labels and uncontrollable imaging condition lead to a low FTSME accuracy. A novel Fresh Tea Shoot Maturity Estimating method via multispectral imaging and Deep Label Distribution Learning (FTSME-DLDL) is proposed to overcome these issues. The input is 25-band images, and the output is the corresponding tea shoot maturity label distribution. We utilize the multiple VGG-16 and auto-encoding network to obtain the multispectral features, and learn the label distribution by minimizing the Kullback-Leibler divergence using deep convolutional neural networks. The experimental results show that the proposed method has a better performance on FTSME than the state-of-the-art methods.

The sidelobe level at tilts around 30-40 degrees in both the E and H planes due to a tapered excitation of units of 2×2 radiation slots is suppressed by introducing slit layers over a corporate-feed waveguide slot array antenna. The slit layers act as averaging the excitation of the adjacent radiating slots for sidelobe suppression in both planes. A 16×16-element array in the 70GHz band is fabricated. At the design frequency, the sidelobe levels at tilts around 30-40 degrees are suppressed from -25.4dB to -31.3dB in the E-plane and from -27.1dB to -38.9dB in the H-plane simultaneously as confirmed by measurements. They are suppressed over the desired range of 71.0-76.0GHz frequencies, compared to the conventional antenna.

The paper deals with the shortest path-based in-trees on a grid graph. There a root is supposed to move among all vertices. As such a spanning mobility pattern, root trajectories based on a Hamilton path or cycle are discussed. Along such a trajectory, each vertex randomly selects the next hop on the shortest path to the root. Under those assumptions, this paper shows that a root trajectory termed an S-path provides the minimum expected symmetric difference. Numerical experiments show that another trajectory termed a Right-cycle also provides the minimum result.