In this paper, we present a small, wide-band, Inverted-L Antenna (ILA) with non-Foster matching. The antenna's size is 9.5×19.5mm2 and it is integrated on a Printed Circuit Board (PCB) of 90×35mm2. A design procedure is presented and sensitivity and stability analysis are performed. Experiments show that the non-Foster matched antenna has (S11 < -10dB) impedance bandwidth of 92.2% at a central frequency of 1.5GHz, whereas the passive antenna (without the non-Foster matching) has an impedance bandwidth of 12.6% at 2.46GHz.

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.

An energy efficient modulator for an ultra-low-power (ULP) 60-GHz IEEE transmitter is presented in this paper. The modulator consists of a differential duobinary coder and a semi-digital finite-impulse-response (FIR) pulse-shaping filter. By virtue of differential duobinary coding and pulse shaping, the transceiver successfully solves the adjacent-channel-power-ratio (ACPR) issue of conventional on-off-keying (OOK) transceivers. The proposed differential duobinary code adopts an over-sampling precoder, which relaxes timing requirement and reduces power consumption. The semi-digital FIR eliminates the power hungry digital multipliers and accumulators, and improves the power efficiency through optimization of filter parameters. Fabricated in a 65nm CMOS process, this modulator occupies a core area of 0.12mm2. With a throughput of 1.7Gbps/2.6Gbps, power consumption of modulator is 24.3mW/42.8mW respectively, while satisfying the IEEE 802.11ad spectrum mask.

A method is proposed for improving the accuracy of the characteristic basis function method (CBFM) using the multilevel approach. With this technique, CBFs taking into account multiple scattering calculated for each block (IP-CBFs; improved primary CBFs) are applied to CBFM using a multilevel approach. By using IP-CBFs, the interaction between blocks is taken into account, and thus it is possible to reduce the number of CBFs while maintaining accuracy, even if the multilevel approach is used. The radar cross section (RCS) of a cube, a cavity, and a dielectric sphere were analyzed using the proposed CBFs, and as a result it was found that accuracy is improved over the conventional method, despite no major change in the number of CBFs.

We propose and demonstrate a method that can demultiplex an optical OFDM signal with various capacity based on time lens-based optical Fourier transform. The proposed tunable optical OFDM signal demultiplexer is composed of a phase modulator and a tunable chromatic dispersion emulator. The spectrum of the variable capacity OFDM signal is transformed into Nyquist time-division multiplexing pulses with the optical Fourier transform, and the OFDM sub-carrier channels are dumultiplexed in the time-domain. We also propose a simple method for approximating and generating quadratic waveform to drive the phase modulator. After explaining the operating principle of the method and the design of some parameters in detail, we show successful demultiplexing of 4×8 and 4×10 Gbit/s optical OFDM signals with our proposed method as the preliminary investigation results.

LSIs are generally designed through four stages including architectural design, logic design, circuit design, and physical design. In architectural design and logic design, designers describe their target hardware in RTL. However, they generally use different languages for each phase. Typically a general purpose programming language such as C or C++ and a hardware description language such as Verilog HDL or VHDL are used for architectural design and logic design, respectively. That is time-consuming way for designing a hardware and more efficient design environment is required. In this paper, we propose a new hardware modeling and high-speed simulation environment for architectural design and logic design. Our environment realizes writing and verifying hardware by one language. The environment consists of (1) a new hardware description language called ArchHDL, which enables to simulate hardware faster than Verilog HDL simulation, and (2) a source code translation tool from ArchHDL code to Verilog HDL code. ArchHDL is a new language for hardware RTL modeling based on C++. The key features of this language are that (1) designers describe a combinational circuit as a function and (2) the ArchHDL library realizes non-blocking assignment in C++. Using these features, designers are able to write a hardware transparently from abstracted level description to RTL description in Verilog HDL-like style. Source codes in ArchHDL is converted to Verilog HDL codes by the translation tool and they are used to synthesize for FPGAs or ASICs. As the evaluation of our environment, we implemented a practical many-core processor in ArchHDL and measured the simulation speed on an Intel CPU and an Intel Xeon Phi processor. The simulation speed for the Intel CPU by ArchHDL achieves about 4.5 times faster than the simulation speed by Synopsys VCS. We also confirmed that the RTL simulation by ArchHDL is efficiently parallelized on the Intel Xeon Phi processor. We convert the ArchHDL code to a Verilog HDL code and estimated the hardware utilization on an FPGA. To implement a 48-node many-core processor, 71% of entire resources of a Virtex-7 FPGA are consumed.

This paper presents the Received-Signal-Strength-Indicator (RSSI) based living-body radar, which uses only a single RF front-end and a few parasitic antennas. This radar measures the RSSI variation at the single active antenna while varying the terminations of the parasitic antennas. The propagation channel is estimated from just the temporal transition of RSSI; our proposal reconstructs the phase information of the signal. In this paper, we aim to estimate the direction of living-body. Experiments are carried out and it is found that most angular errors are within the limit of the angular width of the living-body.

SAR tomography is one of the methods that can perform 3-dimensional (3-D) imaging with multiple SAR datasets by using the Direction-of-arrival (DOA) estimation technique to estimate the height distribution of scatterers. Several reports on SAR tomography have been issued. However, experimental results of the SAR tomography by the Pi-SAR2-X, Japanese airborne SAR operated by the NICT, have not been reported yet. This paper is the first to report the results of experiments on the Japanese SAR platform. High-resolution 2-dimensional image can be obtained by the X-band SAR. However the image is generated by projecting 3-D objects in to a 2-D image plane, hence the target responses having the same slant-range distance locate at the same image pixel. This is well known as the layover problem. When we employ the X-band SAR tomography, we can obtain 3-D high-resolution images without the layover and also foreshortening problem. It will be useful for disaster damage monitoring, especially in urban areas. The main difficulty of the SAR tomography comes from the phase error caused by inaccurate flight-path data. In many cases, the dataset are preprocessed and compensated so as to parallelize their flight-path to carry out the phase calibration and the DOA estimation easily. However, it is often difficult for common users to obtain such preprocessed datasets. In this paper, we propose a simple calibration method by using a flat-surface area with known altitude. Experiments show that the proposed method is effective for the Pi-SAR2-X standard products without parallelized preprocessing or precise flight-path information.

An optical transport network is composed of optical transport systems deployed in thousands of office-buildings. As a common infrastructure to accommodate diversified communication services with drastic traffic growth, it is necessary not only to continuously convey the growing traffic but also to achieve high end-to-end communication quality and availability and provide flexible controllability in cooperation with service layer networks. To achieve high-speed and large-capacity transport systems cost-effectively, system configuration, applied devices, and the manufacturing process have recently begun to change, and the cause of failure or performance degradation has become more complex and diversified. The drastic traffic growth and pattern change of service networks increase the frequency and scale of transport-capacity increase and transport-network reconfiguration in cooperation with service networks. Therefore, drastic traffic growth affects both optical-transport-system configuration and its operational cycles. In this paper, we give an overview of the operational problems emerging in current nationwide optical transport networks, and based on trends analysis for system configuration and network-control schemes, we propose a vision of the future nationwide optical-transport-network architecture expressed using five target features.

To efficiently use network resources, internet service providers need to conduct traffic engineering that dynamically controls traffic routes to accommodate traffic change with limited network resources. The performance of traffic engineering (TE) depends on the accuracy of traffic prediction. However, the size of traffic change has been drastically increasing in recent years due to the growth in various types of network services, which has made traffic prediction difficult. Our approach to tackle this issue is to separate traffic into predictable and unpredictable parts and to apply different control policies. However, there are two challenges to achieving this: dynamically separating traffic according to predictability and dynamically controlling routes for each separated traffic part. In this paper, we propose a macroflow-based TE scheme that uses different routing policies in accordance with traffic predictability. We also propose a traffic-separation algorithm based on real-time traffic analysis and a framework for controlling separated traffic with software-defined networking technology, particularly OpenFlow. An evaluation of actual traffic measured in an Internet2 network shows that compared with current TE schemes the proposed scheme can reduce the maximum link load by 34% (at the most congested time) and the average link load by an average of 11%.

In this paper, we consider a two-hop communication system with an amplify-and-forward (AF) relay under channel estimation errors. According to the channel quality of the link between the base station (BS) and the relay, we investigate two typical relay scenarios. We study the capacity performance for both In-Band Full-Duplex (IBFD) and Half-Duplex (HD) transmission modes. Moreover, we consider two operation modes of the user equipment (UE) for each scenario. Closed-form expressions of ergodic capacities with channel estimation errors are obtained for scenario-1. And we derive accurate approximations of ergodic capacities for scenario-2. Numerical experiments are conducted to verify the analytical results and show that our theoretical derivations are perfectly matched with the simulations. We show that with practical signal-to-noise ratio values and effective interference cancellation techniques, IBFD transmission is preferable in terms of capacity.

Designing shaped offset Gregorian reflector systems to operate with several interchangeable feed horns, over frequency bandwidths of more than a decade, with multiple, often conflicting, performance figures of merit such as aperture efficiency, receiving sensitivity, sidelobe levels, and cross polarization isolation is a difficult optimization problem. An additional complication may be that the radiation patterns of all the feeds to be used in the system are not known at the time of the dish designs, as upgrades to the feeds may happen throughout the lifetime of large reflector systems. This paper presents a systematic parametric study to quantify the effects of the main causes of performance degradation in such a system, i.e. reflector diffraction and feed pattern variations. First, ideal Gaussian feed patterns are used in order to isolate the diffraction effects, and then the ideal patterns are varied to model the effect of using wideband feeds exhibiting radiation pattern variations over frequency. It is shown that the peak position in the shaping parameter space of the receiving sensitivity is not strongly influenced by diffraction - although the peak value is, as expected, reduced at lower frequencies. This allows similar feed patterns to be used in different frequency bands to still produce systems operating near the maximum sensitivity. When using non-ideal feed patterns it is shown that, for most performance metrics, diffraction effects dominate the feed variation performance degradation in smaller dishes. This allows possibly relaxed requirements on the radiation patterns of feeds used to illuminate electrically small reflector systems.

A component-oriented FPGA design platform is proposed for robot system integration. FPGAs are known to be a power-efficient hardware platform, but the development cost of FPGA-based systems is currently too high to integrate them into robot systems. To solve this problem, we propose an FPGA component that allows FPGA devices to be easily integrated into robot systems based on the Robot Operating System (ROS). ROS-compliant FPGA components offer a seamless interface between the FPGA hardware and software running on the CPU. Two experiments were conducted using the proposed components. For the first experiment, the results show that the execution time of an FPGA component for image processing was 1.7 times faster than that of the original software-based component and was 2.51 times more power efficient than an ordinary PC processor, despite substantial communication overhead. The second experiment showed that an FPGA component for sensor fusion was able to process multiple sensor inputs efficiently and with very low latency via parallel processing.

Advanced driver assistance system (ADAS) can recognize traffic signals, vehicles, pedestrians, and so on all over the vehicle. However, because the ADAS is based on images taken in an outdoor environment, it is susceptible to ambient weather such as fog. So, preprocessing such as de-fog and de-hazing techniques is required to prevent degradation of object recognition performance due to decreased visibility. But, if such a fog removal technique is applied in an environment where there is little or no fog, the visual quality may be deteriorated due to excessive contrast improvement. And in foggy road environments, typical fog removal algorithms suffer from color distortion. In this paper, we propose a temporal filter-based fog detection algorithm to selectively apply de-fogging method only in the presence of fog. We also propose a method to avoid color distortion by detecting the sky region and applying different methods to the sky region and the non-sky region. Experimental results show that in the actual images, the proposed algorithm shows an average of more than 97% fog detection accuracy, and improves subjective image quality of existing de-fogging algorithms. In addition, the proposed algorithm shows very fast computation time of less than 0.1ms per frame.

High dynamic range (HDR) images contain more details of the scene as compared to commonly used low dynamic range (LDR) images. The additional information in the HDR images is important for applications such as high-quality graphics rendering, sensing, scene analysis, and surveillance etc. Moreover, HDR images would provide better visualization experience on HDR displays, which might become more common in near future. Therefore, it is important to encode the entire dynamic range of the HDR images. In this paper, a new lossless, four-channel, eight bits per channel, format for encoding floating-point HDR images is proposed. The format is similar to the well-known RGBE format but constructs the E channel differently for better accuracy. Experimental results show that our technique could reduce the rounding error of the RGBE by more than 88%. In addition, there was a reduction of 44.3% in average error for all 33 images in the database used for this study.

This paper proposes a novel method for two-dimensional (2-D) direction-of-arrival (DOA) estimation of multiple signals employing a sparse L-shaped array structured by a sparse linear array (SLA), a sparse uniform linear array (SULA) and an auxiliary sensor. In this method, the elevation angles are estimated by using the SLA and an efficient search approach, while the azimuth angle estimation is performed in two stages. In the first stage, the rough azimuth angle estimates are obtained by utilizing a noise-free cross-covariance matrix (CCM), the estimated elevation angles and data from three sensors including the auxiliary sensor. In the second stage, the fine azimuth angle estimates can be achieved by using the shift-invariance property of the SULA and the rough azimuth angle estimates. Without extra pair-matching process, the proposed method can achieve automatic pairing of the 2-D DOA estimates. Simulation results show that our approach outperforms the compared methods, especially in the cases of low SNR, snapshot deficiency and multiple sources.

Considering that different people are different in their linguistic preference and in order to determine the consensus state when using Computing with Words (CWW) for supporting consensus decision making, this paper first proposes an interval composite scale based 2-tuple linguistic model, which realizes the process of translation from word to interval numerical and the process of retranslation from interval numerical to word. Second, this paper proposes an interval composite scale based personalized individual semantics model (ICS-PISM), which can provide different linguistic representation models for different decision-makers. Finally, this paper proposes a consensus decision making model with ICS-PISM, which includes a semantic translation and retranslation phase during decision process and determines the consensus state of the whole decision process. These models proposed take into full consideration that human language contains vague expressions and usually real-world preferences are uncertain, and provide efficient computation models to support consensus decision making.

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.

Block compressed sensing with random permutation (BCS-RP) has been shown to be very effective for image Encryption-then-Compression (ETC) applications. However, in the BCS-RP scheme, the statistical information of the blocks is disclosed, because the encryption is conducted within each small block of the image. To solve this problem, a two-dimension compressed sensing (2DCS) with 2D random permutation (2DRP) strategy for image ETC applications is proposed in this letter, where the 2DRP strategy is used for encrypting the image and the 2DCS scheme is used for compressing the encrypted image. Compared with the BCS-RP scheme, the proposed approach has two benefits. Firstly, it offers better security. Secondly, it obtains a significant gain of peak signal-to-noise ratio (PSNR) of the reconstructed-images.

Network Functions Virtualization (NFV) is expected to provide network systems that offer significantly lower cost and greatly flexibility to network service providers and their users. Unfortunately, it is extremely difficult to implement Virtualized Network Functions (VNFs) that can equal the performance of Physical Network Functions. To realize NFV systems that have adequate performance, it is critical to accurately grasp VNF workload. In this paper, we focus on the virtual firewall as a representative VNF. The workload of the virtual firewall is mostly determined by firewall rule processing and the Access Control List (ACL) configurations. Therefore, we first reveal the major factors influencing the workload of the virtual firewall and some issues of monitoring CPU load as a traditional way of understanding the workload of virtual firewalls through preliminary experiments. Additionally, we propose a new workload metric for the virtual firewall that is derived by mathematical models of the firewall workload in consideration of the packet processing in each rule and the ACL configurations. Furthermore, we show the effectiveness of the proposed workload metric through various experiments.