Location information is meaningful information for future ITS (Intelligent Transport Systems) world. Especially, the accuracy of the information is required because the accuracy decides the quality of ITS services. For realization of high precision positioning, Kinematic positioning technique has been attracting attention. The Kinematic positioning requires the configuration of many positioning parameters. However, the configuration is difficult because optimal parameter differs according to user's environment. In this paper, we will propose an estimation method of optimal parameter according to the environment. Further, we will propose an elimination method of unreliable positioning results. Hereby, we can acquire extensively only the reliable positioning results. By using the actual vehicle traveling data, the ability and the applicable range of the proposed method will be shown. The result will show that our proposed method improves the acquision rate of reliable positioning results and mitigates the acquision rate of the unreliable positioning results.

The microfabrication technique based on SR (Synchrotron Radiation) direct etching process has recently been applied to construct PTFE microstructures. This paper attempts to fabricate an integrated PTFE-filled waveguide Butler matrix for short millimeter-wave by SR direct etching. First, a cruciform 3-dB directional coupler and an intersection circuit (0-dB coupler) are designed at 180 GHz. Then, a 4×4 Butler matrix with horn antennas is designed and fabricated. Finally, the measured radiation patterns of the Butler matrix are shown.

A novel Doppler spread estimation scheme is proposed for an orthogonal frequency division multiplexing (OFDM) system with a Rayleigh fading channel. The proposal develops a composite power spectral density (PSD) function by averaging the multiple PSD functions computed with multiple sets of the channel frequency response (CFR) coefficients. The Doppler spread is estimated by finding the maximum location of the composite PSD quantities larger than a threshold value given by a fixed fraction of the maximum composite PSD quantity. It is shown by simulation that the proposed scheme performs better than three conventional Doppler spread estimation schemes not only in isotropic scattering environments, but also in nonisotropic scattering environments. Moreover, the proposed scheme is shown to perform well in some Rician channel environments if the Rician K-factor is small.

Numerical inverse Laplace transform (NILT) methods are potential methods for time domain simulations, for instance the analysis of the transient phenomena in systems with lumped and/or distributed parameters. This paper proposes a numerical inverse Laplace transform method based originally on hyperbolic relations. The method is further enhanced by properly adapting several convergence acceleration techniques, namely, the epsilon algorithm of Wynn, the quotient-difference algorithm of Rutishauser and the Euler transform. The resulting accelerated models are compared as for their accuracy and computational efficiency. Moreover, an expansion to two dimensions is presented for the first time in the context of the accelerated hyperbolic NILT method, followed by the error analysis. The expansion is done by repeated application of one-dimensional partial numerical inverse Laplace transforms. A detailed static error analysis of the resulting 2D NILT is performed to prove the effectivness of the method. The work is followed by a practical application of the 2D NILT method to simulate voltage/current distributions along a transmission line. The method and application are programmed using the Matlab language.

We describe the design and simulation of a suitable branching ratio for an optical coupler for a cable re-routing operation support system with no service interruption, and report our experimental results. We also show the risk analysis, and report that the branching ratio was 0.47 where the probability was 99.7% that the maximum acceptable cable loss of a detour line was more than that of the current line.

3D ball tracking is of great significance in ping-pong game analysis, which can be utilized to applications such as TV contents and tactic analysis, with some of them requiring real-time implementation. This paper proposes a CPU-GPU platform based Particle Filter for multi-view ball tracking including 4 proposals. The multi-peak estimation and the ball-like observation model are proposed in the algorithm design. The multi-peak estimation aims at obtaining a precise ball position in case the particles' likelihood distribution has multiple peaks under complex circumstances. The ball-like observation model with 4 different likelihood evaluation, utilizes the ball's unique features to evaluate the particle's similarity with the target. In the GPU implementation, the double-queue structure and the vectorized data combination are proposed. The double-queue structure aims at achieving task parallelism between some data-independent tasks. The vectorized data combination reduces the time cost in memory access by combining 3 different image data to 1 vector data. Experiments are based on ping-pong videos recorded in an official match taken by 4 cameras located in 4 corners of the court. The tracking success rate reaches 99.59% on CPU. With the GPU acceleration, the time consumption is 8.8 ms/frame, which is sped up by a factor of 98 compared with its CPU version.

Many mobile systems need to achieve both high performance and low memory usage, and the total performance of such the systems can be largely affected by the effectiveness of GC. Hence, the recent popularization of mobile devices makes the GC performance play one of the important roles on the wide range of platforms. The response performance degradation caused by suspending all processes for GC has been a well-known potential problem. Therefore, GC algorithms have been actively studied and improved, but they still have not reached any fundamental solution. In this paper, we focus on the point that the same objects are redundantly marked during the GC procedure implemented on DalvikVM, which is one of the famous runtime environments for the mobile devices. Then we propose a hardware support technique for improving marking routine of GC. We installed a set of tables to a processor for managing marked objects, and redundant marking for marked objects can be omitted by referring these tables. The result of the simulation experiment shows that the percentage of redundant marking is reduced by more than 50%.

Major cloud service providers, including Amazon and Microsoft, have started employing field-programmable gate arrays (FPGAs) to build high-performance and low-power-consumption cloud capability. However, utilizing an FPGA-enabled cloud is still challenging because of two main reasons. First, the introduction of software and hardware co-design leads to high development complexity. Second, FPGA virtualization and accelerator scheduling techniques are not fully researched for cluster deployment. In this paper, we propose an open-source FPGA-as-a-service (FaaS) platform, the hCODE, to simplify the design, management and deployment of FPGA accelerators at cluster scale. The proposed platform implements a Shell-and-IP design pattern and an open accelerator repository to reduce design and management costs of FPGA projects. Efficient FPGA virtualization and accelerator scheduling techniques are proposed to deploy accelerators on the FPGA-enabled cluster easily. With the proposed hCODE, hardware designers and accelerator users can be organized on one platform to efficiently build open-hardware ecosystem.

Identification of urban road targets using radar systems is usually heavily dependent on the aspect angle between the target velocity and line of sight of the radar. To improve the performance of the classification result when the target is in a cross range position relative to the radar, a method based on range micro Doppler signature is proposed in this paper. Joint time-frequency analysis is applied in every range cell to extract the time Doppler signature. The spectrograms from all of the target range cells are combined to form the range micro Doppler signature to allow further identification. Experiments were conducted to investigate the performance of the proposed method, and the results proved the effectiveness of the method presented.

In this work, we address the stationary target localization problem by using Doppler frequency shift (DFS) measurements. Based on the measurement model, the maximum likelihood estimation (MLE) of the target position is reformulated as a constrained weighted least squares (CWLS) problem. However, due to its non-convex nature, it is difficult to solve the problem directly. Thus, in order to yield a semidefinite programming (SDP) problem, we perform a semidefinite relaxation (SDR) technique to relax the CWLS problem. Although the SDP is a relaxation of the original MLE, it can facilitate an accurate estimate without post processing. Simulations are provided to confirm the promising performance of the proposed method.

This paper describes the design and measurements of two-gain-mode MMIC power amplifier modules (PAMs) for Band 1 and Band 5 WCDMA data communications. The PAMs are based on the two-stage single-chain amplifier topology with an L-shaped FET step attenuator (ATT) placed at the interstage, featuring not only high-efficiency operation but also both a small phase discontinuity and a small input return loss variation between the two gain modes: a high-gain mode (0-dB thru state for the ATT) and a low-gain mode (14-dB attenuation state for the ATT). The PAMs are assembled on a 3 mm × 3 mm FR-4 laminate together with several surface mount devices, and a high-directivity, 20-dB bilayer-type directional coupler is integrated on the laminate for accurate forward-power monitoring even under a 2.5:1-VSWR load mismatching condition. To validate the design and analysis for the PAMs using the L-shaped ATT, two PAM products — a Band 1 PAM and a Band 5 PAM — were fabricated using our in-house GaAs-BiFET process. The main RF measurements under the condition of a WCDMA (R99) modulated signal and a 3.4-V supply voltage are as follows. The Band 1 PAM can deliver a power-added efficiency (PAE) as high as 46% at an output power (Pout) of 28.25 dBm while maintaining a ±5-MHz-offset adjacent channel power ratio (ACLR1) of approximately -40 dBc or less and a small phase discontinuity of less than 5°. The Band 5 PAM can also deliver a high PAE of 46% at the same Pout and ACLR1 levels with small phase discontinuity of less than 4°. This small discontinuity is due to the phase-shift compensation capacitance embedded in the ATT. The measured input return loss is well maintained at better than 10 dB at the two modes. In addition, careful coupler design achieves a small detection error of less than 0.5 dB even under a 2.5:1-VSWR load mismatching condition.

In this paper, we propose a low-complexity widely-linear minimum mean square error (WL-MMSE) signal detection based on the Chebyshev polynomials accelerated symmetric successive over relaxation (SSORcheb) algorithm for uplink (UL) over-loaded large-scale multiple-input multiple-output (MIMO) systems. The technique of utilizing Chebyshev acceleration not only speeds up the convergence rate significantly, and maximizes the data throughput, but also reduces the cost. By utilizing the random matrix theory, we present good estimates for the Chebyshev acceleration parameters of the proposed signal detection in real large-scale MIMO systems. Simulation results demonstrate that the new WL-SSORcheb-MMSE detection not only outperforms the recently proposed linear iterative detection, and the optimal polynomial expansion (PE) WL-MMSE detection, but also achieves a performance close to the exact WL-MMSE detection. Additionally, the proposed detection offers superior sum rate and bit error rate (BER) performance compared to the precision MMSE detection with substantially fewer arithmetic operations in a short coherence time. Therefore, the proposed detection can satisfy the high-density and high-mobility requirements of some of the emerging wireless networks, such as, the high-mobility Internet of Things (IoT) networks.

Hardware implementation of neural networks usually have high computational complexity that increase exponentially with the size of a circuit, leading to more uncertain and unreliable circuit performance. This letter presents a novel Radial Basis Function (RBF) neural network based on parallel fault tolerant stochastic computing, in which number is converted from deterministic domain to probabilistic domain. The Gaussian RBF for middle layer neuron is implemented using stochastic structure that reduce the hardware resources significantly. Our experimental results from two pattern recognition tests (the Thomas gestures and the MIT faces) show that the stochastic design is capable to maintain equivalent performance when the stream length set to 10Kbits. The stochastic hidden neuron uses only 1.2% hardware resource compared with the CORDIC algorithm. Furthermore, the proposed algorithm is very flexible in design tradeoff between computing accuracy, power consumption and chip area.

In this letter, a new rapid and accurate synchronization scheme based on PMF-FFT for high dynamic GPS receiver is proposed, with a fine Doppler frequency estimation inserted between the acquisition and tracking modules. Fine Doppler estimation is firstly achieved through a simple interpolation of the PMF-FFT outputs in terms of LSE criterion. Then a high dynamic tracking loop based on UKF is designed to verify the synchronization speed and accuracy. Numerical results show that the fine frequency estimation can closely approach the CRB, and the high dynamic receiver can obtain fine synchronization rapidly just through a very narrow bandwidth. The simplicity and low complexity give the proposed scheme a strong and practical-oriented ability, even for weak GPS signals.

This paper demonstrates a PLL compiler that generates the final GDSII data from a specification of input and output frequencies with PVT corner conditions. A Pulse Width Controlled PLLs (PWPLL) is composed of digital blocks, and thus suitable for being designed using a standard cell library and being layed out with a commercially available place-and-route (P&R) tool. A PWPLL has 8 design parameters. Our PLL compiler decides the 8 parameters and confirms the PLL operation with the following functions: 1) calculates rough parameter values based on an analytical model, 2) generates SPICE and gate-level verilog netlists with given parameter values, 3) runs SPICE simulations and analyzes the waveform, to examine the oscillation frequency or the voltage of specified nodes at a given time, 4) changes the parameter values to an appropriate direction depending on the waveform analyses to obtain the optimized parameter values, 5) generates scripts that can be used in commercial design tools and invokes the tools with the gate-level verilog netlist to get the final LVS/DRC-verified GDSII data from a P&R and a verification tools, and finally 6) generates the necessary characteristic summary sheets from the post-layout SPICE simulations extracted from the GDSII. Our compiler was applied to an 0.18µm standard CMOS technology to design a PLL with 600MHz output, 600/16MHz input frequency, and confirms the PLL operation with 1.2mW power and 85µm×85µm layout area.

In this paper, a multichannel speech enhancement system which adopts a denoising auto-encoder as part of the beamformer is proposed. The proposed structure of the generalized sidelobe canceller generates enhanced multi-channel signals, instead of merely one channel, to which the following denoising auto-encoder can be applied. Because the beamformer exploits spatial information and compensates for differences in the transfer functions of each channel, the proposed system is expected to resolve the difficulty of modelling relative transfer functions consisting of complex numbers which are hard to model with a denoising auto-encoder. As a result, the modelling capability of the denoising auto-encoder can concentrate on removing the artefacts caused by the beamformer. Unlike conventional beamformers, which combine these artefacts into one channel, they remain separated for each channel in the proposed method. As a result, the denoising auto-encoder can remove the artefacts by referring to other channels. Experimental results prove that the proposed structure is effective for the six-channel data in CHiME, as indicated by improvements in terms of speech enhancement and word error rate in automatic speech recognition.

Hotspots occur frequently in 3D multi-core processors (3D-MCPs), and they may adversely impact both the reliability and lifetime of a system. We present a new thermally constrained task scheduler based on a thermal-pattern-aware voltage assignment (TPAVA) to reduce hotspots in and optimize the performance of 3D-MCPs. By analyzing temperature profiles of different voltage assignments, TPAVA pre-emptively assigns different initial operating-voltage levels to cores for reducing temperature increase in 3D-MCPs. The proposed task scheduler consists of an on-line allocation strategy and a new voltage-scaling strategy. In particular, the proposed on-line allocation strategy uses the temperature-variation rates of the cores and takes into two important thermal behaviors of 3D-MCPs that can effectively minimize occurrences of hotspots in both thermally homogeneous and heterogeneous 3D-MCPs. Furthermore, a new vertical-grouping voltage scaling (VGVS) strategy that considers thermal correlation in 3D-MCPs is used to handle thermal emergencies. Experimental results indicate that, when compared to a previous online thermally constrained task scheduler, the proposed task scheduler can reduce hotspot occurrences by approximately 66% (71%) and improve throughput by approximately 8% (2%) in thermally homogeneous (heterogeneous) 3D-MCPs. These results indicate that the proposed task scheduler is an effective technique for suppressing hotspot occurrences and optimizing throughput for 3D-MCPs subject to thermal constraints.

The extraction of micro-motion parameters is deeply influenced by the precision of estimation on translational motion parameters. Based on the periodicity of micro-motion, the quadratic polynomial fitting is carried out among range delays to align envelope. The micro-motion component of phase information is eliminated by conjugate multiplication after which the translational motion parameters are estimated. Then the translational motion is precisely compensated through the third order polynomial fitting. Results of simulation demonstrate that the algorithm put forward here can realize the precise compensation for translational motion parameters even under an environment with low signal noise ratio (SNR).

This paper presents an interactive locomotion controller using motion capture data and an inverted pendulum model (IPM). The motion data of a character is decomposed into those of upper and lower bodies, which are then dimension-reduced via what we call hierarchical Gaussian process dynamical model (H-GPDM). The locomotion controller receives the desired walking direction from the user. It is integrated into the IPM to determine the pose of the center of mass and the stance-foot position of the character. They are input to the H-GPDM, which interpolates the low-dimensional data to synthesise a redirected motion sequence on an uneven surface. The locomotion controller allows the upper and lower bodies to be independently controlled and helps us generate natural locomotion. It can be used in various real-time applications such as games.

This introduces a method which uses LIDAR to identify humans and track their positions, body orientation, and movement trajectories in any public space to read their various types of behavioral responses to surroundings. We use a network of LIDAR poles, installed at the shoulder level of typical adults to reduce potential occlusion between persons and/or objects even in large-scale social environments. With this arrangement, a simple but effective human tracking method is proposed that works by combining multiple sensors' data so that large-scale areas can be covered. The effectiveness of this method is evaluated in an art gallery of a real museum. The result revealed good tracking performance and provided valuable behavioral information related to the art gallery.