The introduction of deep neural networks (DNNs) leads to a significant improvement of the automatic speech recognition (ASR) performance. However, the whole ASR system remains sophisticated due to the dependent on the hidden Markov model (HMM). Recently, a new end-to-end ASR framework, which utilizes recurrent neural networks (RNNs) to directly model context-independent targets with connectionist temporal classification (CTC) objective function, is proposed and achieves comparable results with the hybrid HMM/DNN system. In this paper, we investigate per-dimensional learning rate methods, ADAGRAD and ADADELTA included, to improve the recognition of the end-to-end system, based on the fact that the blank symbol used in CTC technique dominates the output and these methods give frequent features small learning rates. Experiment results show that more than 4% relative reduction of word error rate (WER) as well as 5% absolute improvement of label accuracy on the training set are achieved when using ADADELTA, and fewer epochs of training are needed.

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.

Automated biometrics identification using finger vein images has increasingly generated interest among researchers with emerging applications in human biometrics. The traditional feature-level fusion strategy is limited and expensive. To solve the problem, this paper investigates the possible use of infrared hybrid finger patterns on the back side of a finger, which includes both the information of finger vein and finger dorsal textures in original image, and a database using the proposed hybrid pattern is established. Accordingly, an Intersection enhanced Gabor based Direction Coding (IGDC) method is proposed. The Experiment achieves a recognition ratio of 98.4127% and an equal error rate of 0.00819 on our newly established database, which is fairly competitive.

Short texts usually encounter the problem of data sparseness, as they do not provide sufficient term co-occurrence information. In this paper, we show how to mitigate the problem in short text classification through word embeddings. We assume that a short text document is a specific sample of one distribution in a Gaussian-Bayesian framework. Furthermore, a fast clustering algorithm is utilized to expand and enrich the context of short text in embedding space. This approach is compared with those based on the classical bag-of-words approaches and neural network based methods. Experimental results validate the effectiveness of the proposed method.

We address the problem of entity identification on a microblog with special attention to indirect reference cases in which entities are not referred to by their names. Most studies on identifying entities referred to them by their full/partial name or abbreviation, while there are many indirectly mentioned entities in microblogs, which are difficult to identify in short text such as microblogs. We therefore tackled indirect reference cases by developing features that are particularly important for certain types of indirect references and modeling dependency among referred entities by a Conditional Random Field (CRF) model. In addition, we model non-sequential order dependency while keeping the inference tractable by dynamically building dependency among entities. The experimental results suggest that our features were effective for indirect references, and our CRF model with adaptive dependency was robust even when there were multiple mentions in a microblog and achieved the same high performance as that with the fully connected CRF model.

A map-reduce framework is popular for big data analysis. In the typical map-reduce framework, both master node and worker nodes can use hard-disk drives (HDDs) as local disks for the map-reduce computation. However, because of the inherit mechanical problems of HDDs, the I/O performance is a bottleneck for the map-reduce framework when I/O-intensive applications (e.g., sorting) are performed. Replacing HDDs with solid-state drives (SSDs) is not economical, although SSDs have better performance than HDDs. In this paper, we propose a virtualization-based hybrid storage system for the map-reduce framework. The objective of the paper is to combine the advantages of the fast access property of SSDs and the low cost of HDDs by realizing an economical design and improving I/O performance of a map-reduce framework in a virtualization environment. We propose three storage combinations: SSD-based, HDD-based, and a hybrid of SSD-based and HDD-based storage systems which balances speed, capacity, and lifetime. According to experiments, the hybrid of SSD-based and HDD-based storage systems offers superior performance and economy.

In this paper, we consider filter-and-forward relay beamforming using orthogonal frequency-division multiplexing (OFDM) in the presence of inter-block interference (IBI). We propose a filter design method based on a constrained max-min problem, which aims to suppress IBI and also avoid deep nulls in the frequency domain. It is shown that IBI can be suppressed completely owing to the employment of beamforming with multiple relays or multiple receive antennas at each relay when perfect channel state information (CSI) is available. In addition, we modify the proposed method to cover the case where only the partial CSI for relay-receiver channels is available. Numerical simulation results show that the proposed method significantly improves the performance as the number of relays and antennas increases due to spatial diversity, and the modified method can make use of the channel correlation to improve the performance.

In this letter, standard particle swarm optimization (PSO) with the center-symmetric trimmed correlation matrix and the orthogonal projection technique is firstly presented for blind carrier frequency offset estimation under interleaved orthogonal frequency division multiple access (OFDMA) uplink systems. It doesn't require eigenvalue decomposition and only needs a single OFDMA data block. Second, this letter also presents adaptive multiple inertia weights with Newton method to speed up the convergence of standard PSO iteration process. Meanwhile, the advantage of inherent interleaved OFDMA signal structure also is exploited to conquer the problems of local optimization and the effect of ambiguous peaks for the proposed approaches. Finally, several simulation results are provided for illustration and comparison.

In this letter, we present a spectrally efficient multicast method which enables a transmitter to simultaneously transmit multiple multicast streams without any interference among multicast groups. By using unique combiners at receivers with multiple antennas within each multicast group, the proposed method simplifies multiple channels between the transmitter and the receivers to an equivalent channel. In addition, we establish the sufficient condition for the system configuration which should be satisfied for the channel simplification and provide a combiner design technique for the receivers. To remove interference among multicast groups, the precoder for the transmitter is designed by utilizing the equivalent channels. By exploiting time resources efficiently, the channel simplification (CS) based method achieves a higher sum rate than the time division multiplexing (TDM) based method, which the existing multicast techniques fundamentally employ, at high signal-to-noise ratio (SNR) regime. Furthermore, we present a multicast method combining the CS based method with the TDM based method to utilize the benefits of both methods. Simulation results successfully demonstrate that the combined multicast method obtains a better sum rate performance at overall SNR regime.

Quadriphase sequences with good correlation properties are required in higher order digital modulation schemes, e.g., for timing measurements, channel estimation or synchronization. In this letter, based on interleaving technique and pairs of mismatched binary sequences with perfect cross-correlation function (PCCF), two new methods for constructing quadriphase sequences with mismatched filtering which exist for even length N ≡ 2(mod4) are presented. The resultant perfect mismatched quadriphase sequences have high energy efficiencies. Compared with the existing methods, the new methods have flexible parameters and can give cyclically distinct perfect mismatched quadriphase sequences.

We investigate a phased array-fed dual reflector antenna applying one-dimensional beam-scanning of the center-fed type, using an elliptical aperture to provide wide area observation. The distinguishing feature of this antenna is its elliptical aperture shape, in which the aperture diameter differs between the forward satellite direction and the cross-section orthogonal to it. The shape in the plane of the forward satellite direction, which does not have a beam-scanning function, is a ring-focus Cassegrain antenna, and the shape in the plane orthogonal to that, which does have a beam-scanning function, is an imaging reflector antenna. This paper describes issues which arose during design of the elliptical aperture shape and how they were solved, and presents design results using elliptical aperture dimensions of 1600 mm × 600 mm, in which the beam width differs by more than two times in the orthogonal cross-section. The effectiveness of the antenna was verified by fabricating a prototype antenna based on the design results. Measurement results confirmed that an aperture efficiency of 50% or more could be achieved, and that a different beam width was obtained in the orthogonal plane in accordance with design values.

Negative group delay characteristics can be used to improve signal-integrity performance such as equalizer for compensation of the group delay of transmission line (TL). This brief-paper newly attempts to propose a concept of the embedded Folded-Stepped Impedance Resonator (F-SIR) structure with open-stub resonator, for negative group delay and slope characteristics at high-frequency as well as low-insertion loss. The concept of the proposed TL is based on the combination of resonance and anti-resonance due to open-stub resonator in order to establish wideband negative group delay and negative slope characteristics. The proposed TL is fabricated on PCB, and then the concept is validated by measurement and simulation.

This paper presents non-iterative cooperative/parallel Kalman filtering algorithms for decentralized network navigation, in which mobile nodes cooperate in both spatial and temporal domains to infer their positions. We begin by presenting an augmented minimum-mean-square error (MMSE) estimator for centralized navigation network, and then decouple it into a set of local sub-ones each corresponding to a mobile node; all these sub-estimators work in parallel and cooperatively — with the state estimates exchanging between neighbors — to provide results similar to those obtained by the augmented one. After that, we employ the approximation methods that adopted in the conventional nonlinear Kalman filters to calculate the second-order terms involved in these sub-estimators, and propose a decentralized cooperative/parallel Kalman filtering based network navigation framework. Finally, upon the framework, we present two cooperative/parallel Kalman filtering algorithms corresponding to the extended and unscented Kalman filters respectively, and compare them with conventional decentralized methods by simulations to show the superiority.

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.

Recently, Doppler radars have been used in various applications from the detection and the classification of indoor human activities to the detection of airplanes. To improve both the degrees of freedom (DOF) and the estimation accuracy of the direction-of-arrival (DOA) of targets, multiple-input multiple-output (MIMO) radar has received much attention in recent years. The temporal spatial virtual array based on Doppler shift of a moving target has been one of methods to improve DOA estimation accuracy. However, the DOA estimation accuracy based on the method depends on the velocity and the direction of the target on which we focus. Also, the temporal spatial virtual array should be generated based on the information of the single target. Thus, it is difficult to implement the method if there are multiple targets. In this paper, we propose a new method that provides high accuracy of DOA estimation by using the temporal spatial virtual array without dependence on the velocity, the direction and the number of existing targets. We demonstrate the DOA estimation accuracy and the effectiveness of the proposed method via simulations.

An overview of the evolution of intelligent transport systems (ITS) supported by advances in information and communication technologies is presented. Focusing on a sensing platform as one of the ITS applications, this paper presents a survey on vehicular ad hoc network-based geographic routing. In addition to the minimum requirement of street-awareness based on street maps, traffic and packet-awareness are considered essential to achieve acceptable packet delivery performance. In particular, in addition to statistical information, real-time traffic and packet level information are indispensable for making routing protocols feasible and effective. Considering traffic conditions that are highly space- and time-dependent, static nodes can be used to assist with geographic routing, and a protocol workable under a partial deployment of static nodes is considered.

Due to the increasing demand for 3D video transmission over wireless networks, managing the quality of experience (QoE) of wireless 3D video clients is becoming increasingly important. However, the variability of compressed 3D video bit streams and the wireless channel condition as well as the complexity of 3D video viewing experience assessment make it difficult to properly allocate wireless transmission resources. In this paper, we discuss the characteristics of H.264 3D videos and QoE assessment of 3D video clients, and further propose a transmission scheme for 3D video transmission over a wireless communication system. The purpose of our scheme is to minimize the average ratio of stalls among all video streaming clients. By taking into account the playout lead and its change, we periodically evaluate the degree of urgency of each client as regards bitstream receipt based on fuzzy logic, and then allocate the transmission resource blocks to clients jointly considering their degrees of urgency and channel conditions. The adaptive modulation and coding scheme (MCS) is applied to ensure a low transmission error rate. Our proposed scheme is suitable for practical implementation since it has low complexity, and can be easily applied in 2D video transmission and in non-OFDM systems. Simulation results, based on three left-and-right-views 3D videos and the Long Term Evolution (LTE) system, demonstrate the validity of our proposed scheme.

In this paper, the integration of dynamic plant-wide optimization and distributed generalized predictive control (DGPC) is presented for serially connected processes. On the top layer, chance-constrained programming (CCP) is employed in the plant-wide optimization with economic and model uncertainties, in which the constraints containing stochastic parameters are guaranteed to be satisfied at a high level of probability. The deterministic equivalents are derived for linear and nonlinear individual chance constraints, and an algorithm is developed to search for the solution to the joint probability constrained problem. On the lower layer, the distributed GPC method based on neighborhood optimization with one-step delay communication is developed for on-line control of the whole system. Simulation studies for furnace temperature set-points optimization problem of the walking-beam-type reheating furnace are illustrated to verify the effectiveness and practicality of the proposed scheme.

In this paper, we study the spectral efficiency of the uplink multi-user large-scale distributed antenna systems (DAS) with imperfect channel state information. We propose the system model of multi-user DAS and illustrate the necessity of pilot reuse. Then, we derive the sum-rate of the system under pilot contamination. Furthermore, we investigate the asymptotical performance when the number of antennas goes to infinity. To reduce the pilot contamination, we present two novel pilot assignment algorithms to improve the spectral efficiency. Finally, we evaluate our proposed strategies through extensive simulations which show that compared with random pilot reuse, the min-max algorithm shows impressive performance with low complexity.

DDoS remains a major threat to Software Defined Networks. To keep SDN secure, effective detection techniques for DDoS are indispensable. Most of the newly proposed schemes for detecting such attacks on SDN make the SDN controller act as the IDS or the central server of a collaborative IDS. The controller consequently becomes a target of the attacks and a heavy loaded point of collecting traffic. A collaborative intrusion detection system is proposed in this paper without the need for the controller to play a central role. It is deployed as a modified artificial neural network distributed over the entire substrate of SDN. It disperses its computation power over the network that requires every participating switch to perform like a neuron. The system is robust without individual targets and has a global view on a large-scale distributed attack without aggregating traffic over the network. Emulation results demonstrate its effectiveness.