This paper describes, for the first time, an experimental study on the layout design considerations of GaAs HBT MMIC switchable-amplifier-chain-based power amplifiers (SWPAs) for CDMA handsets. The transient response of the quiescent current and output power (Pout) in GaAs HBT power amplifiers that consist of a main chain and a sub-chain is often affected by a thermal coupling between power stages and their bias circuits in the same chain or a thermal coupling between power stages and/or their bias circuits in different chains. In particular, excessively strong thermal coupling inside the MMIC SWPA causes failure in 3GPP-compliant inner loop power control tests. An experimental study reveals that both the preheating in the main/sub-chains and appropriate thermal coupling inside the main chain are very effective in reducing the turn-on delay for the two-parallel-amplifier-chain topology; for example, i) the sub-power stage is arranged near the main power stage, ii) the sub-driver stage is placed near the main driver stage and iii) the main driver bias circuit is placed near the main power stage and the sub-power stage. The SWPA operating in Band 9 (1749.9 to 1784.9 MHz), which was designed and fabricated from the foregoing considerations, shows a remarkable improvement in the Pout turn-on delay: a reduced power level error of 0.74 dB from turn-off to turn-on in the sub-amplifier chain and a reduced power level error of over 0.30 dB from turn-off to turn-on in the main amplifier chain. The main RF power measurements conducted with a 3.4-V supply voltage and a Band 9 WCDMA HSDPA modulated signal are as follows. The SWPA delivers a Pout of 28.5 dBm, a power gain (Gp) of 28 dB, and a PAE of 39% while restricting the ACLR1 to less than -40 dBc in the main amplifier chain. In the sub-amplifier chain, 17 dBm of Pout, 23.5 dB of Gp, and 27% of PAE are obtained at the same ACLR1 level.

In this paper, we propose an algorithm that enhances the number of pixels for high-speed imaging. High-speed cameras have a principle problem that the number of pixels reduces when the number of frames per second (fps) increases. To enhance the number of pixels, we suppose an optical structure that block-randomly selects some percent of pixels in an image. Then, we need to reconstruct the entire image. For this, a state-of-the-art method takes three-dimensional reconstruction strategy, which requires a heavy computational cost in terms of time. To reduce the cost, the proposed method reconstructs the entire image frame-by-frame using a new cost function exploiting two types of sparsity. One is within each frame and the other is induced from the similarity between adjacent frames. The latter further means not only in the image domain, but also in a sparsifying transformed domain. Since the cost function we define is convex, we can find the optimal solution using a convex optimization technique with small computational cost. We conducted simulations using grayscale image sequences. The results show that the proposed method produces a sequence, mostly the same quality as the state-of-the-art method, with dramatically less computational time.

Particle filter algorithm is an important algorithm in the field of target tracking. however, this algorithm faces the problem of sample impoverishment which is caused by the introduction of re-sampling and easily affected by illumination variation. This problem seriously affects the tracking performance of a particle filter algorithm. To solve this problem, we introduce a particle filter target tracking algorithm based on a dynamic niche genetic algorithm. The application of this dynamic niche genetic algorithm to re-sampling ensures particle diversity and dynamically fuses the color and profile features of the target in order to increase the algorithm accuracy under the illumination variation. According to the test results, the proposed algorithm accurately tracks the target, significantly increases the number of particles, enhances the particle diversity, and exhibits better robustness and better accuracy.

This paper studies the supervisory control of partially observed quantitative discrete event systems (DESs) under the fixed-initial-credit energy objective. A quantitative DES is modeled by a weighted automaton whose event set is partitioned into a controllable event set and an uncontrollable event set. Partial observation is modeled by a mapping from each event and state of the DES to the corresponding masked event and masked state that are observed by a supervisor. The supervisor controls the DES by disabling or enabling any controllable event for the current state of the DES, based on the observed sequences of masked states and masked events. We model the control process as a two-player game played between the supervisor and the DES. The DES aims to execute the events so that its energy level drops below zero, while the supervisor aims to maintain the energy level above zero. We show that the proposed problem is reducible to finding a winning strategy in a turn-based reachability game.

An online nonnegative matrix factorization (NMF) algorithm based on recursive least squares (RLS) is described in a matrix form, and a simplified algorithm for a low-complexity calculation is developed for frame-by-frame online audio source separation system. First, the online NMF algorithm based on the RLS method is described as solving the NMF problem recursively. Next, a simplified algorithm is developed to approximate the RLS-based online NMF algorithm with low complexity. The proposed algorithm is evaluated in terms of audio source separation, and the results show that the performance of the proposed algorithms are superior to that of the conventional online NMF algorithm with significantly reduced complexity.

In this paper, an accurate experimental noise model to improve the EEHEMT nonlinear model using the Verilog-A language in Agilent ADS is presented for the first time. The present EEHEMT model adopts channel noise to model the noise behavior of pseudomorphic high electron mobility transistor (pHEMT). To enhance the accuracy of the EEHEMT noise model, we add two extra noise sources: gate shot noise and induced gate noise current. Here we demonstrate the power spectral density of the channel noise Sid and gate noise Sig versus gate-source voltage for 0.25 µm pHEMT devices. Additionally, the related noise source parameters, i.e., P, R, and C are presented. Finally, we compare four noise parameters between the simulation and model, and the agreement between the measurement and simulation results shows that this proposed approach is dependable and accurate.

In this paper, we propose a new paradigm of deterministic PSO, named piecewise-linear particle swarm optimizer (PPSO). In PPSO, each particle has two search dynamics, a convergence mode and a divergence mode. The trajectory of each particle is switched between the two dynamics and is controlled by parameters. We analyze convergence condition of each particle and investigate parameter conditions to allow particles to converge to an equilibrium point through numerical experiments. We further compare solving performances of PPSO. As a result, we report here that the solving performances of PPSO are substantially the same as or superior to those of PSO.

This paper presents the design and characterization of an E-band 16×16-slot monopulse array antenna with full-corporate-feed fabricated by the commercially available batch process of diffusion bonding of laminated copper plates. The antenna is multi-layered, and consists of vertically-interconnected radiating elements, a corporate-feed circuit and a comparator. It has four input ports for different excitations. Sum and difference beams in different cut-planes for monopulse operation can be generated. The antenna has a quasi-planar profile, and a total size of 13.31 λ0×13.31λ0×1.52λ0 (λ0 is the wavelength at the design frequency of 78.5GHz). The antenna demonstrates a wide operation bandwidth of 17.2 (70-87.2) GHz for VSWR < 2. At 78.5GHz: 1) for the sum beam, there is a 32.6-dBi realized gain (83% antenna efficiency) and a 33.3-dBi directivity (95% aperture efficiency); 2) for the difference beams in the E-, H-, 45°-, and 135°-planes, the null depths are -53.0, -58.0, -57.8, and -65.6dB, respectively. Across the full operation band where the sum main-beam and difference null are able to consistently point at the boresight, the antenna also demonstrates excellent performance in terms of high gain, high efficiency, high isolation, low cross-polarization, and distinguished monopulse capability.

A 1.9GHz film bulk acoustic resonator (FBAR)-based low-phase-noise complementary cross-coupled voltage-controlled oscillator (VCO) is presented. The FBAR-VCO is designed and fabricated in 0.18µm CMOS process. The DC latch and the low frequency instability are resolved by employing the NMOS source coupling capacitor and the DC blocked cross-coupled pairs. Since no additional voltage headroom is required, the proposed FBAR-VCO can be operated at a low power supply voltage of 1.1V with a wide voltage swing of 0.9V. An effective phase noise optimization is realized by a reasonable trade-off between the output resistance and the trans-conductance of the cross-coupled pairs. The measured performance shows the proposed FBAR-VCO achieves a phase noise of -148dBc/Hz at 1MHz offset with a figure of merit (FoM) of -211.6dB.

A chirp spread spectrum (CSS) system uses a chirp signal which changes the instantaneous frequency according to time for spreading a transmission bandwidth. In the CSS system, the transmission performance can be simply improved by increasing the time-bandwidth product which is known as the processing gain. However, increasing the transmission bandwidth is limited because of the spectrum regulation. In this letter, we propose a correlation-based chirp rate allocation method to improve the transmission performance by analyzing the cross-correlation coefficient in the same time-bandwidth product. In order to analyze the transmission performance of the proposed method, we analytically derive the cross-correlation coefficient according to the time-bandwidth separation product and simulate the transmission performance. The simulation results show that the proposed method can analytically allocate the optimal chirp rate and improve the transmission performance.

As seen in stream data processing, it is necessary to extract a particular data field from bulk data, where we can use a field-data extractor. Particularly, an (M,N)-field-data extractor reads out any consecutive N bytes from an M-byte register by connecting its input/output using multiplexers (MUXs). However, the number of required MUXs increases too much as the input/output byte widths increase. It is known that partitioning a MUX network leads to reducing the number of MUXs. In this paper, we firstly pick up a multi-layered MUX network, which is generated by repeatedly partitioning a MUX network into a collection of single-layered MUX networks. We show that the multi-layered MUX network is equivalent to the barrel shifter from which redundant MUXs and wires are removed, and we prove that the number of required MUXs becomes the smallest among MUX-network-partitioning based field-data extractors. Next, we propose a rotator-based MUX network for a field-data extractor, which is based on reading out a particular data in an input register to a rotator. The byte width of the rotator is the same as its output register and hence we no longer require any extra wires nor MUXs. By rotating the input data appropriately, we can finally have a right-ordered data into an output register. Experimental results show that a multi-layered MUX network reduces the number of required gates to construct a field-data extractor by up to 97.0% compared with the one using a naive approach and its delay becomes 1.8ns-2.3ns. A rotator-based MUX network with a control circuit also reduces the number of required gates to construct a field-data extractor by up to 97.3% compared with the one using a naive approach and its delay becomes 2.1ns-2.9ns.

This paper describes a logarithmic compression ADC using a subranging TDC and the transient response of a comparator. We utilized the settling time of the comparator for a logarithmic compression instead of a logarithmic amplifier. The settling time of the comparator is inversely proportional to the logarithm of an input voltage. In the proposed ADC, an input voltage is converted into a pulse whose width represents the settling time of the comparator. Subsequently, the TDC converts the pulse width into a binary code. The supply voltage of the proposed ADC can be reduced more than a conventional logarithmic ADC because an analog to digital conversion takes place in the time domain. We confirmed through a 0.18-µm CMOS circuit simulation that the proposed ADC achieves a resolution of 11 bits, a sampling rate of 20 MS/s, a dynamic range of 59 dB and a power consumption of 9.8 mW at 1.5 V operation.

Particle swarm optimizer network (PSON) is one of the multi-swarm PSOs. In PSON, a population is divided into multiple sub-PSOs, each of which searches a solution space independently. Although PSON has a good solving performance, it may be trapped into a local optimum solution. In this paper, we introduce into PSON a dynamic stochastic network topology called “PSON with stochastic connection” (PSON-SC). In PSON-SC, each sub-PSO can be connected to the global best (gbest) information memory and refer to gbest stochastically. We show clearly herein that the diversity of PSON-SC is higher than that of PSON, while confirming the effectiveness of PSON-SC by many numerical simulations.

The quality of codebook is very important in visual image classification. In order to boost the classification performance, a scheme of codebook generation for scene image recognition based on parallel key SIFT analysis (PKSA) is presented in this paper. The method iteratively applies classical k-means clustering algorithm and similarity analysis to evaluate key SIFT descriptors (KSDs) from the input images, and generates the codebook by a relaxed k-means algorithm according to the set of KSDs. With the purpose of evaluating the performance of the PKSA scheme, the image feature vector is calculated by sparse code with Spatial Pyramid Matching (ScSPM) after the codebook is constructed. The PKSA-based ScSPM method is tested and compared on three public scene image datasets. The experimental results show the proposed scheme of PKSA can significantly save computational time and enhance categorization rate.

This letter proposes a Non-uniform Cell-based Index (NCI) to enable clients to quickly process window queries in the wireless spatial data broadcast environment. To improve the access time, NCI reduces the probe wait time by equalized spacing between indexes, using non-uniformly partitioned cells of data space. Through the performance evaluation, we show the proposed NCI outperforms the existing index schemes for window queries to spatial data in respect of access time.

This paper proposes a method for evaluating a physical gait motion based on a 3D human skeleton measured by a depth sensor. While similar methods measure and evaluate the motion of only a part of interest (e.g., knee), the proposed method comprehensively evaluates the motion of the full body. The gait motions with a variety of physical disabilities due to lesioned body parts are recorded and modeled in advance for gait anomaly detection. This detection is achieved by finding lesioned parts a set of pose features extracted from gait sequences. In experiments, the proposed features extracted from the full body allowed us to identify where a subject was injured with 83.1% accuracy by using the model optimized for the individual. The superiority of the full-body features was validated in in contrast to local features extracted from only a body part of interest (77.1% by lower-body features and 65% by upper-body features). Furthermore, the effectiveness of the proposed full-body features was also validated with single universal model used for all subjects; 55.2%, 44.7%, and 35.5% by the full-body, lower-body, and upper-body features, respectively.

Linked data endpoints are online query gateways to semantically annotated linked data sources. In order to query these data sources, SPARQL query language is used as a standard. Although a linked data endpoint (i.e. SPARQL endpoint) is a basic Web service, it provides a platform for federated online querying and data linking methods. For linked data consumers, SPARQL endpoint availability and discovery are crucial for live querying and semantic information retrieval. Current studies show that availability of linked datasets is very low, while the locations of linked data endpoints change frequently. There are linked data respsitories that collect and list the available linked data endpoints or resources. It is observed that around half of the endpoints listed in existing repositories are not accessible (temporarily or permanently offline). These endpoint URLs are shared through repository websites, such as Datahub.io, however, they are weakly maintained and revised only by their publishers. In this study, a novel metacrawling method is proposed for discovering and monitoring linked data sources on the Web. We implemented the method in a prototype system, named SPARQL Endpoints Discovery (SpEnD). SpEnD starts with a “search keyword” discovery process for finding relevant keywords for the linked data domain and specifically SPARQL endpoints. Then, the collected search keywords are utilized to find linked data sources via popular search engines (Google, Bing, Yahoo, Yandex). By using this method, most of the currently listed SPARQL endpoints in existing endpoint repositories, as well as a significant number of new SPARQL endpoints, have been discovered. We analyze our findings in comparison to Datahub collection in detail.

When performing measurements in an outdoor field environment, various interference factors occur. So, many studies have been performed to increase the accuracy of the localization. This paper presents a novel probability-based approach to estimating position based on Apollonius circles. The proposed algorithm is a modified method of existing trilateration techniques. This method does not need to know the exact transmission power of the source and does not require a calibration procedure. The proposed algorithm is verified in several typical environments, and simulation results show that the proposed method outperforms existing algorithms.

Traditional speech enhancement (SE) algorithms usually have fluctuant performance when they deal with different types of noisy speech signals. In this paper, we propose multi-task Bayesian compressive sensing based speech enhancement (MT-BCS-SE) algorithm to achieve not only comparable performance to but also more stable performance than traditional SE algorithms. MT-BCS-SE algorithm utilizes the dependence information among compressive sensing (CS) measurements and the sparsity of speech signals to perform SE. To obtain sufficient sparsity of speech signals, we adopt overcomplete dictionary to transform speech signals into sparse representations. K-SVD algorithm is employed to learn various overcomplete dictionaries. The influence of the overcomplete dictionary on MT-BCS-SE algorithm is evaluated through large numbers of experiments, so that the most suitable dictionary could be adopted by MT-BCS-SE algorithm for obtaining the best performance. Experiments were conducted on well-known NOIZEUS corpus to evaluate the performance of the proposed algorithm. In these cases of NOIZEUS corpus, MT-BCS-SE is shown that to be competitive or even superior to traditional SE algorithms, such as optimally-modified log-spectral amplitude (OMLSA), multi-band spectral subtraction (SSMul), and minimum mean square error (MMSE), in terms of signal-noise ratio (SNR), speech enhancement gain (SEG) and perceptual evaluation of speech quality (PESQ) and to have better stability than traditional SE algorithms.

In this paper, we investigate a relationship between many-one-like autoreducibility and completeness for classes of functions computed by polynomial-time nondeterministic Turing transducers. We prove two results. One is that any many-one complete function for these classes is metric many-one autoreducible. The other is that any strict metric many-one complete function for these classes is strict metric many-one autoreducible.