Given a graph G=(V,E) where V and E are a vertex and an edge set, respectively, specified with a subset VNT of vertices called a non-terminal set, the spanning tree with non-terminal set VNT is a connected and acyclic spanning subgraph of G that contains all the vertices of V where each vertex in a non-terminal set is not a leaf. In the case where each edge has the weight of a nonnegative integer, the problem of finding a minimum spanning tree with a non-terminal set VNT of G was known to be NP-hard. However, the complexity of finding a spanning tree on general graphs where each edge has the weight of one was unknown. In this paper, we consider this problem and first show that it is NP-hard even if each edge has the weight of one on general graphs. We also show that if G is a cograph then finding a spanning tree with a non-terminal set VNT of G is linearly solvable when each edge has the weight of one.

This paper proposes a new quantization framework on activation function of deep neural networks (DNN). We implement fixed-point DNN by quantizing the activations into powers-of-two integers. The costly multiplication operations in using DNN can be replaced with low-cost bit-shifts to massively save computations. Thus, applying DNN-based speech recognition on embedded systems becomes much easier. Experiments show that the proposed method leads to no performance degradation.

In practice, emotional speech utterances are often collected from different devices or conditions, which will lead to discrepancy between the training and testing data, resulting in sharp decrease of recognition rates. To solve this problem, in this letter, a novel transfer semi-supervised non-negative matrix factorization (TSNMF) method is presented. A semi-supervised negative matrix factorization algorithm, utilizing both labeled source and unlabeled target data, is adopted to learn common feature representations. Meanwhile, the maximum mean discrepancy (MMD) as a similarity measurement is employed to reduce the distance between the feature distributions of two databases. Finally, the TSNMF algorithm, which optimizes the SNMF and MMD functions together, is proposed to obtain robust feature representations across databases. Extensive experiments demonstrate that in comparison to the state-of-the-art approaches, our proposed method can significantly improve the cross-corpus recognition rates.

Deep Neural Network (DNN) is a powerful machine learning model that has been successfully applied to a wide range of pattern classification tasks. Due to the great ability of the DNNs in learning complex mapping functions, it has been possible to train and deploy DNNs pretty much as a black box without the need to have an in-depth understanding of the inner workings of the model. However, this often leads to solutions and systems that achieve great performance, but offer very little in terms of how and why they work. This paper introduces Sensitivity-characterised Activity Neorogram (SCAN), a novel approach for understanding the inner workings of a DNN by analysing and visualising the sensitivity patterns of the neuron activities. SCAN constructs a low-dimensional visualisation space for the neurons so that the neuron activities can be visualised in a meaningful and interpretable way. The embedding of the neurons within this visualisation space can be used to compare the neurons, both within the same DNN and across different DNNs trained for the same task. This paper will present the observations from using SCAN to analyse DNN acoustic models for automatic speech recognition.

This paper aims to investigate the performance improvements made possible by combining various major language model (LM) technologies together and to reveal the interactions between LM technologies in spontaneous automatic speech recognition tasks. While it is clear that recent practical LMs have several problems, isolated use of major LM technologies does not appear to offer sufficient performance. In consideration of this fact, combining various LM technologies has been also examined. However, previous works only focused on modeling technologies with limited text resources, and did not consider other important technologies in practical language modeling, i.e., use of external text resources and unsupervised adaptation. This paper, therefore, employs not only manual transcriptions of target speech recognition tasks but also external text resources. In addition, unsupervised LM adaptation based on multi-pass decoding is also added to the combination. We divide LM technologies into three categories and employ key ones including recurrent neural network LMs or discriminative LMs. Our experiments show the effectiveness of combining various LM technologies in not only in-domain tasks, the subject of our previous work, but also out-of-domain tasks. Furthermore, we also reveal the relationships between the technologies in both tasks.

The 3-D channel routing is a fundamental problem on the physical design of 3-D integrated circuits. The 3-D channel is a 3-D grid G and the terminals are vertices of G located in the top and bottom layers. A net is a set of terminals to be connected. The objective of the 3-D channel routing problem is to connect the terminals in each net with a Steiner tree (wire) in G using as few layers as possible and as short wires as possible in such a way that wires for distinct nets are disjoint. This paper shows that the problem is intractable. We also show that a sparse set of ν 2-terminal nets can be routed in a 3-D channel with O(√ν) layers using wires of length O(√ν).

Audio-Visual Speech Recognition (AVSR) is one of techniques to enhance robustness of speech recognizer in noisy or real environments. On the other hand, Deep Neural Networks (DNNs) have recently attracted a lot of attentions of researchers in the speech recognition field, because we can drastically improve recognition performance by using DNNs. There are two ways to employ DNN techniques for speech recognition: a hybrid approach and a tandem approach; in the hybrid approach an emission probability on each Hidden Markov Model (HMM) state is computed using a DNN, while in the tandem approach a DNN is composed into a feature extraction scheme. In this paper, we investigate and compare several DNN-based AVSR methods to mainly clarify how we should incorporate audio and visual modalities using DNNs. We carried out recognition experiments using a corpus CENSREC-1-AV, and we discuss the results to find out the best DNN-based AVSR modeling. Then it turns out that a tandem-based method using audio Deep Bottle-Neck Features (DBNFs) and visual ones with multi-stream HMMs is the most suitable, followed by a hybrid approach and another tandem scheme using audio-visual DBNFs.

This paper presents an innovative fabrication process for a planar circuits at millimeter-wave frequency. Screen printing technology provides low cost and high performance coplanar waveguides (CPW) lines in planar devices operated at millimeter-wave frequency up to 110GHz. Printed transmission lines provide low insertion losses of 0.30dB/mm at 110GHz and small return loss like as impedance standard lines. In the paper, Multiline Thru-Reflect-Line (TRL) calibration was also demonstrated by using the impedance standard substrates (ISS) fabricated by screen printing. Regarding calibration capability validation, verification devices were measured and compare the results to the result obtained by the TRL calibration using commercial ISS. The comparison results obtained by calibration of screen printing ISS are almost the same as results measured based on conventional ISS technology.

In this paper, we propose a compressed sensing scheme using sparse-graph codes and peeling decoder (SGPD). By using a mix method for construction of sensing matrices proposed by Pawar and Ramchandran, it generates local sensing matrices and implements sensing and signal recovery in an adaptive manner. Then, we show how to optimize the construction of local sensing matrices using the theory of sparse-graph codes. Like the existing compressed sensing schemes based on sparse-graph codes with “good” degree profile, SGPD requires only O(k) measurements to recover a k-sparse signal of dimension n in the noiseless setting. In the presence of noise, SGPD performs better than the existing compressed sensing schemes based on sparse-graph codes, still with a similar implementation cost. Furthermore, the average variable node degree for sensing matrices is empirically minimized for SGPD among various existing CS schemes, which can reduce the sensing computational complexity.

This paper introduces a reversible watermarking algorithm that exploits an adaptable predictor and sorting parameter customized for each image and each payload. Our proposed method relies on a well-known prediction-error expansion (PEE) technique. Using small PE values and a harmonious PE sorting parameter greatly decreases image distortion. In order to exploit adaptable tools, Gaussian weight predictor and expanded variance mean (EVM) are used as parameters in this work. A genetic algorithm is also introduced to optimize all parameters and produce the best results possible. Our results show an improvement in image quality when compared with previous conventional works.

A compact multiple-input multiple-output (MIMO) dielectric resonator antenna (DRA) was proposed and studied. The DRA consists of three antenna ports. The antennas operate at 2.4GHz, where one of the antenna ports was placed at the center and resonates in the monopole mode, and the two other ports were located at the sides and resonate in the TEy111 mode. Both simulation and measurements were carried out, and reasonably good agreement was obtained. In addition, a study for miniaturization with different permittivities for the DRA and a comparison of the throughput with the reference antennas of a commercial wireless LAN router were performed. Our proposed MIMO DRA gave similar performance as that of the reference antennas but was more compact in size.

Silver electrical contacts are separated at constant speed and break arcs are generated between them in a 200V-450VDC and 10A resistive circuit. The motion of the break arcs is restricted by some surrounding alumina plates. Transverse magnetic field of a permanent magnet is applied to the break arcs. Changing the supply voltage and the height of a wall located at the upper side of the break arcs, the arc lengthening time and motion of the break arcs are investigated. As a result, the higher supply voltage causes an increase of the arc lengthening time. The arc lengthening time increases significantly when the break arcs expand into the whole of the surrounding walls.

Silver contacts are separated at constant speed and break arcs are generated in a 300V-450V DC and 10A resistive circuit. The transverse magnetic field of a permanent magnet is applied to the break arcs. Motion of the break arcs, arc duration and the number of reignitions are investigated when side surfaces of the contacts are covered with insulator pipes. Following results are shown. The motion of the break arcs and the arc duration when the anode is covered with the pipe are the same as those without pipes. When the cathode is covered with the pipe, the motion of break arcs change from that without the pipes and reignitions occur more frequently. The arc duration becomes longer than that without the pipes because of the occurrence of reignitions. The number of reignition increases with increasing the supply voltage in 300V-400V. The period of occurrence of the reignition with pipes is shorter than that when the cathode is covered with the pipe.

Break arcs are rotated with the radial magnetic field formed by a magnet embedded in a fixed cathode contact. The break arcs are generated in a 48VDC resistive circuit. The circuit current when the contacts are closed is 10A. The depth of the magnet varies from 1mm to 4mm to change the strength of the radial magnetic field for rotating break arcs. Images of break arcs are taken by two high-speed cameras from two directions and the rotational motion of the break arcs is observed. The rotational period of rotational motion of the break arcs is investigated. The following results are obtained. The break arcs rotate clockwise on the cathode surface seen from anode side. This rotation direction conforms to the direction of the Lorentz force that affects to the break arcs with the radial magnetic field. The rotational period gradually decreases during break operation. When the depth of magnet is larger, the rotational period becomes longer.

The scan segmentation method is an efficient solution to deal with the test power problem; However, the use of multiple capture cycles may cause capture violations, thereby leading to fault coverage loss. This issue is much more severe in at-speed testing. In this paper, two scan partition schemes based on complex networks clustering ara proposed to minimize the capture violations without increasing test-data volume and extra area overhead. In the partition process, we use a more accurate notion, spoiled nodes, instead of violation edges to analyse the dependency of flip-flops (ffs), and we use the shortest-path betweenness (SPB) method and the Laplacian-based graph partition method to find the best combination of these flip-flops. Beyond that, the proposed methods can use any given power-unaware set of patterns to test circuits, reducing both shift and capture power in at-speed testing. Extensive experiments have been performed on reference circuit ISCAS89 and IWLS2005 to verify the effectiveness of the proposed methods.

In this letter, fast intra mode decision algorithms for HEVC Screen Contents Coding (SCC) are proposed. HEVC SCC has been developed to efficiently code mixed contents consisting of natural video, graphics, and texts. Comparing to HEVC version 1, the SCC encoding complexity significantly increases due to the newly added intra block copy mode. To reduce the heavy encoding complexity, the evaluation orders of multiple intra modes are rearranged and several early termination schemes based on intermediate coding information are developed. Based on our evaluation, it is found that the proposed method can achieve encoding time reduction of 13∼30% with marginal coding gain or loss, compared with HEVC SCC test model 2.0 in all intra (AI) case.

We propose a system named ETIS (Energy-based Tree Illustration System) for automatically generating tree illustrations characteristic of two-dimensional ones with features such as exaggerated branch curves, leaves, and flowers. The growth behavior of the trees can be controlled by adjusting the energy. The canopy shape and the region to fill with leaves and flowers are also controlled by hand-drawn guide lines.

In densely deployed wireless local area network (WLAN) environments, the arbitrary deployment of WLAN access points (APs) can cause serious cell overlaps among APs. In such situations, the ability to realize adaptable coverage using transmission power control (TPC) is effective for improving the area spectral efficiency. Meanwhile, it should be guaranteed that no coverage holes occur and that connectivity between APs and wireless stations (STAs) is maintained. In this paper, the self-organization of coverage domains of APs using TPC is proposed. The proposed technique reduces the incidence of coverage overlaps without generating area coverage holes. To detect coverage holes, STAs and/or APs are used as sensors that inform each AP of whether or not the points at which they exist are covered by the APs. However, there is a problem with this approach in that when the density of STAs is not sufficiently large, the occurrence of area coverage holes is inevitable because the points at which the sensors do not exist are not guaranteed to be covered by APs. This paper overcomes the problem by focusing APs that belong to network's outer boundary (boundary APs) and prohibiting the APs from operating at low transmission power levels, the idea being that the coverage domains of such APs always include the region covered by only those APs. The boundary APs are determined by performing Delaunay triangulation of the set of points at which all APs exist. Simulation results confirm the effectiveness of the proposed TPC scheme in terms of its ability to reduce the total overlap area while avoiding the occurrence of area coverage holes.

In the conventional cellular macrocell implementation strategy, the main base station transmits the radio signals in the omnidirectional manner in order to provide a wide range of cellular transmission to the users. In reality, however, the users move from one place to another depending on their activities, hence, sometimes this creates areas where no user exists inside the macrocell. Nevertheless, the base station continues to transmit the radio signals to all the coverage areas due to its involuntary manner, thus causing waste of energy. In our previous work, an energy efficient LTE macrocell base station scheme based on hourly user location distribution, which utilized opportunistic beamforming, was proposed in order to provide the cellular transmission only to the area where the user density is high. The drawback of this scheme was that there were many users who cannot receive the cellular transmission because of the limitation of the beamforming shape. In this paper, to overcome this difficulty, a new energy efficient macrocell strategy will be proposed. Here, additional low power consumption femtocell access points are deployed inside the macrocell to support the energy efficient opportunistic beamforming based on the hourly user location distribution. Concretely, the femtocell access points are woken up only when the active calling users exist inside its range. The proposed new strategy will be evaluated in terms of the hourly successful calling user ratio, the total power consumption and the hourly average downlink throughput compared with the previously proposed beamforming transmission strategy and the conventional omnidirectional transmission. The results will show the effectiveness of the proposed strategy in providing an energy efficient cellular macrocell system with high quality cellular services.

We reported a secure scan design approach using shift register equivalents (SR-equivalents, for short) that are functionally equivalent but not structurally equivalent to shift registers [10 and also introduced generalized shift registers (GSRs, for short) to apply them to secure scan design [11]-[13]. In this paper, we combine both concepts of SR-equivalents and GSRs and consider the synthesis problem of SR-equivalent GSRs, i.e., how to modify a given GSR to an SR-equivalent GSR. We also consider the enumeration problem of SR-equivalent GFSRs, i.e., the cardinality of the class of SR-equivalent GSRs to clarify the security level of the secure scan architecture.