Exploring a parsimonious model that is just enough to represent the temporal dependency of time serial signals such as audio or speech is a practical requirement for many signal processing applications. A well suited method for intuitively and efficiently representing magnitude spectra is to use convolutive non-negative matrix factorization (CNMF) to discover the temporal relationship among nearby frames. However, the model order selection problem in CNMF, i.e., the choice of the number of convolutive bases, has seldom been investigated ever. In this paper, we propose a novel Bayesian framework that can automatically learn the optimal model order through maximum a posteriori (MAP) estimation. The proposed method yields a parsimonious and low-rank approximation by removing the redundant bases iteratively. We conducted intuitive experiments to show that the proposed algorithm is very effective in automatically determining the correct model order.

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.

A novel virtual sensors-based positioning method has been presented in this paper, which can make use of both direct paths and indirect paths. By integrating the virtual sensor idea and Bayesian state and observation framework, this method models the indirect paths corresponding to persistent virtual sensors as virtual direct paths and further reformulates the wireless positioning problem as the maximum likelihood estimation of both the mobile terminal's positions and the persistent virtual sensors' positions. Then the method adopts the EM (Expectation Maximization) and the particle filtering schemes to estimate the virtual sensors' positions and finally exploits not only the direct paths' measurements but also the indirect paths' measurements to realize the mobile terminal's positions estimation, thus achieving better positioning performance. Simulation results demonstrate the effectiveness of the proposed method.

Antenna radiation patterns have side-lobes that add to ambiguity in the form of ghosting and object repetition in SAR images. An L-band 1.27GHz, 2×5 element proximity-coupled corner-truncated patch array antenna synthesized using the Dolph-Chebyshev method to reduce side-lobe levels is proposed. The designed antenna was sim-ulated, optimized, and fabricated for antenna performance parameter measurements. Antenna performance characteristics show good agree-ment with simulated results. A set of antennas were fabricated and then used together with a custom synthetic aperture radar system and SAR imaging performed on a point target in an anechoic chamber. Imaging results are also discussed in this paper showing improvement in image output. The antenna and its connected SAR systems developed in this work are different from most previous work in that this work is utilizing circular polarization as opposed to linear polarization.

This paper introduces a novel Direct Digital Frequency Synthesizer based on Complementary Dual-Phase Latch-Based sequencing method. Compared to conventional Direct Digital Frequency Synthesizer using Flip-Flop as synchronizing element, the proposed architecture allows to double the data sampling rate while trading-off area and Power Efficiency. Digital domain modulations can be easily implemented by using a Direct Digital Frequency Synthesizer. However, due to performance limitations, CMOS-based applications have been almost exclusively restricted to VHF, UHF and L bands. This work aims to increase the operation speed and extend the applicability of this technology to Multi-band Multi-standard wireless systems operating up to 2.7 GHz. The design features a 24 bits pipelined Phase Accumulator and a 14x10 bits Phase to Amplitude Converter. The Phase to Amplitude Converter module is compressed by using Quarter Wave Symmetry technique and is entirely made up of combinational logic inserted into 12 Complementary Dual-Phase Latch-Based pipeline stages. The logic is represented in the form of Sum of Product terms obtained from a 14x10 bits sinusoidal Look-Up-Table. The proposed Direct Digital Frequency Synthesizer is designed and simulated based on 65nm CMOS standard-cell technology. A maximum data sampling rate of 6.8 GS/s is expected. Estimated Spurious Free Dynamic Range and Power Efficiency are 61 dBc and 22 mW/(GS/s) respectively.

This paper proposes a new and simple microstrip bandpass filter structure for the design of a fully canonical transversal array filter. The filter is constructed by the parallel arrangement of microstrip even- and odd-mode half-wavelength resonators. In this filter, transmission zeros (TZs) are not produced by cross couplings used in conventional filter designs, but by an intrinsic negative coupling of the odd-mode resonators having open ends with respect to the even-mode resonators with shorted ends. Thus, the control of the resonant frequency and the external Q factor of each resonator makes it possible to form both a specified passband and TZs. As an example, a fully canonical bandpass filter with 2-GHz center frequency, 6% bandwidth, and four TZs is synthesized with a coupling-matrix optimization, and its structural parameters are designed. The designed filter achieves a rapid roll-off and low-loss passband response, which can be confirmed numerically and experimentally.

Sensor networks are often used to understand underlying phenomena that are reflected through sensing data. In real world applications, this understanding supports decision makers attempting to access a disaster area or monitor a certain event regularly and thus necessary actions can be triggered in response to the problems. Practitioners designing such systems must overcome difficulties due to the practical limitations of the data and the fidelity of a network condition. This paper explores the design of a network solution for the data acquisition domain with the goal of increasing the efficiency of data gathering efforts. An unmanned aerial vehicle (UAV) is introduced to address various real-world sensor network challenges such as limited resources, lack of real-time representative data, and mobility of a relay station. Towards this goal, we introduce a novel cooperative path selection framework to effectively collect data from multiple sensor sources. The framework consists of six main parts ranging from the system initialization to the UAV data acquisition. The UAV data acquisition is useful to increase situational awareness or used as inputs for data manipulation that support response efforts. We develop a system-based simulation that creates the representative sensor networks and uses the UAV for collecting data packets. Results using our proposed framework are analyzed and compared to existing approaches to show the efficiency of the scheme.

An L-shaped probe with a surrounding aperture such as a waveguide can generate circular polarization (CP) waves. Circular waveguide antennas using an L-shaped probe have broadband characteristics both in axial ratio (AR) and in input impedance, however cross-polarization (XPOL) is easily generated due to its asymmetrical structure resulting in a radiation pattern that has narrow CP azimuth range. In this paper, design techniques to reduce the XPOL generated from a circular waveguide antenna using an L-shaped probe are proposed. As a result, XPOL is reduced by around 10 dB, and CP is radiated over a wide angle range of 120-150° covering frequencies from 7.35 to 9.75GHz.

The unconditionally stable (US) Laguerre-FDTD method has recently attracted significant attention for its high efficiency and accuracy in modeling fine structures. One of the most attractive characteristics of this method is its marching-on-in-order solution scheme. This paper presents Hermite-Rodriguez functions as another type of orthogonal basis to implement a new 2-D US solution scheme.

We propose an edge-preserving multiscale image decomposition method using filters for non-equispaced signals. It is inspired by the domain transform, which is a high-speed edge-preserving smoothing method, and it can be used in many image processing applications. One of the disadvantages of the domain transform is sensitivity to noise. Even though the proposed method is based on non-equispaced filters similar to the domain transform, it is robust to noise since it employs a multiscale decomposition. It uses the Laplacian pyramid scheme to decompose an input signal into the piecewise-smooth components and detail components. We design the filters by using an optimization based on edge-preserving smoothing with a conversion of signal distances and filters taking into account the distances between signal intervals. In addition, we also propose construction methods of filters for non-equispaced signals by using arbitrary continuous filters or graph spectral filters in order that various filters can be accommodated by the proposed method. As expected, we find that, similar to state-of-the-art edge-preserving smoothing techniques, including the domain transform, our approach can be used in many applications. We evaluated its effectiveness in edge-preserving smoothing of noise-free and noisy images, detail enhancement, pencil drawing, and stylization.

As the number of surveillance cameras keeps increasing, the demand for automated traffic-monitoring systems is growing. In this paper, we propose a practical vehicle detection method for such systems. In the last decade, vehicle detection mainly has been performed by employing an image scan strategy based on sliding windows whereby a pre-trained appearance model is applied to all image areas. In this approach, because the appearance models are built from vehicle sample images, the normalization of the scales and aspect ratios of samples can significantly influence the performance of vehicle detection. Thus, to successfully apply sliding window schemes to detection, it is crucial to select the normalization sizes very carefully in a wise manner. To address this, we present a novel vehicle detection technique. In contrast to conventional methods that determine the normalization sizes without considering given scene conditions, our technique first learns local region-specific size models based on scene-contextual clues, and then utilizes the obtained size models to normalize samples to construct more elaborate appearance models, namely local size-specific classifiers (LSCs). LSCs can provide advantages in terms of both accuracy and operational speed because they ignore unnecessary information on vehicles that are observable in faraway areas from each sliding window position. We conduct experiments on real highway traffic videos, and demonstrate that the proposed method achieves a 16% increased detection accuracy with at least 3 times faster operational speed compared with the state-of-the-art technique.

For phase stabilization of two-tone coherent millimeter-wave/microwave carrier generation, two types of phase detection schemes were devised based on lightwave interferometric technique, the Mach-Zehnder interferometric method and the pseudo Mach-Zehnder interferometric method. The former system showed clear eye patterns at both OOK and PSK modulations of 1 Gbit/s on the 12.5-GHz carrier. The latter system demonstrated the error-free transmission at OOK modulation of 11 Gbit/s on the 100-GHz carrier.

In this paper, we consider a two-hop relay network with a decode-and-forward (DF) relaying protocol where a multi-input/multi-output (MIMO) relay station (RS) is deployed in a cell edge to extend cell coverage of a base station (BS). We propose two MIMO relaying schemes to improve the quality of the BS-RS link, which is a key to improve data rates in the DF relaying: 1) spatial multiplexed MIMO antenna relaying (SM-MAR) with a uniform channel decomposition (UCD) precoder, and 2) MIMO relaying with section diversity (SD-MAR). In the SM-MAR, we greatly simplify user allocation by the UCD precoder and propose a sophisticated rate maximization technique to resolve the non-convexity of rate maximization problems. Through simulations, we show that the proposed UCD based power allocation exhibits up to two times higher achievable throughput than other techniques. In addition, the proposed SD-MAR supports the BS with a single transmit antenna and increases the signal quality of the BS-RS link with the selection diversity at the RS, which is much simpler to be implemented. For the SD-MAR, we derive a closed form expression for the achievable throughput and show that the selection diversity plays more important role on the achievable throughput than the multiuser diversity.

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.

Circuit performance of SiC-MOSFET-based bidirectional isolated DC/DC converters is investigated based on circuit simulation with the physically accurate compact device model HiSIM_HV. It is demonstrated that the combined optimization of the MOSFETs Ron and of the inductances in the transformer can enable a conversion efficiency of more than 97%. The simulation study also verifies that the possible efficiency improvements are diminished due to the MOSFET-performance degradation, namely the carrier-mobility reduction, which results in a limitation of the possible Ron reduction. It is further demonstrated that an optimization of the MOSFET-operation conditions is important to utilize the resulting higher MOSFET performance for achieving additional converter efficiency improvements.

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.

NoSQL systems have become vital components to deliver big data services due to their high horizontal scalability. However, existing NoSQL systems rely on experienced administrators to configure and tune the wide range of configurable parameters for optimized performance. In this work, we present a configuration management framework for NoSQL systems, called xConfig. With xConfig, its users can first identify performance sensitive parameters and capture the tuned parameters for different workloads as configuration policies. Next, based on tuned policies, xConfig can be implemented as the corresponding configuration optimiaztion system for the specific NoSQL system. Also it can be used to analyze the range of configurable parameters that may impact the runtime performance of NoSQL systems. We implement a prototype called HConfig based on HBase, and the parameter tuning strategies for HConfig can generate tuned policies and enable HBase to run much more efficiently on both individual worker node and entire cluster. The massive writing oriented evaluation results show that HBase under write-intensive policies outperforms both the default configuration and some existing configurations while offering significantly higher throughput.

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.

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.