This paper proposes a novel satellite channel allocation algorithm for a demand assigned multiple access (DAMA) controller. In satellite communication systems, the channels' total bandwidth and total power are limited by the satellite's transponder bandwidth and transmission power (satellite resources). Our algorithm is based on multi-carrier transmission and adaptive modulation methods. It optimizes channel elements such as the number of sub-carriers, modulation level, and forward error correction (FEC) coding rate. As a result, the satellite's transponder bandwidth and transmission power can be simultaneously used to the maximum and the overall system capacity, i.e., total transmission bit rate, will increase. Simulation results show that our algorithm increases the overall system capacity by 1.3 times compared with the conventional fixed modulation algorithm.

The power of laser radar received echoes varies over a large range due to many factors such as target distance, size, reflection ratio, etc, which leads to the difficulty of decoding codes from the received noise buried signals for spectrum code modulated laser radar. Firstly, a pseudo-random noise (PN) code modulated laser radar model is given, and the problem to be addressed is discussed. Then, a novel method based on Inter Symbol Interference (ISI) is proposed for resolving the problem, providing that only Additive White Gaussian Noise (AWGN) exists. The ISI effect is introduced by using a high pass filter (HPF). The results show that ISI improves laser radar receiver decoding ratio, thus the peak of the correlation function of decoded codes and modulation codes. Finally, the effect of proposed method is verified by a simple experiment.

This letter presents the architecture of multi-gigabit parallel demodulator suitable for demodulating high order QAM modulated signal and easy to implement on FPGA platform. The parallel architecture is based on frequency domain implementation of matched filter and timing phase correction. Parallel FIFO based delete-keep algorithm is proposed for timing synchronization, while a kind of reduced constellation phase-frequency detector based parallel decision feedback PLL is designed for carrier synchronization. A fully pipelined parallel adaptive blind equalization algorithm is also proposed. Their parallel implementation structures suitable for FPGA platform are investigated. Besides, in the demonstration of 2 Gbps demodulator for 16QAM modulation, the architecture is implemented and validated on a Xilinx V6 FPGA platform with performance loss less than 2 dB.

Ultra-wide band (UWB) pulse radar with high range resolution and dielectric permeability is promising as an internal imaging technique for non-destructive testing or breast cancer detection. Various imaging algorithms for buried objects within a dielectric medium have been proposed, such as aperture synthesis, the time reversal approach and the space-time beamforming algorithm. However, these algorithms mostly require a priori knowledge of the dielectric medium boundary in image focusing, and often suffer from inadequate accuracy to identify the detailed structure of buried targets, such as an edge or specular surface owing to employing the waveform focusing scheme. To overcome these difficulties, this paper proposes an accurate and non-parametric (i.e. using an arbitrary shape without target modeling) imaging algorithm for targets buried in a homogeneous dielectric medium by advancing the RPM (Range Points Migration) algorithm to internal imaging issues, which has been demonstrated to provide an accurate image even for complex-shaped objects in free-space measurement. Numerical simulations, including those for two-dimensional (2-D) and three-dimensional (3-D) cases, verify that the proposed algorithm enhances the imaging accuracy by less than 1/10 of the wavelength and significantly reduces the computational cost by specifying boundary extraction compared with the conventional SAR-based algorithm.

Electromagnetic scattering at high-frequencies is computationally heavy. Radar cross section (RCS) of electrically large concave and convex objects are solved by using the hybrid method. For convex and concave surfaces, Modified-Vector Physical Optics (MPO) with enhanced accuracy and Iterative Physical Optics (IPO) taking multiple-reflections into account, are selectively and independently applied for convex and concave parts of the scatterer. The accuracy of RCS by this hybrid method is tested with the MoM based simulator Wipl-D as the reference. The RCS from relatively small scatterers with the dimension of the order of a few wavelengths can be successfully predicted.

Ultra-wideband (UWB) pulse radars have a definite advantage in high-range resolution imaging, and are suitable for short-range measurements, particularly at disaster sites or security scenes where optical sensors are rarely suitable because of dust or strong backlighting. Although we have already proposed an accurate imaging algorithm called Range Points Migration (RPM), its reconstructible area is too small to identify the shape of an object if it is far from the radar and the size of the aperture is inadequate. To resolve this problem, this paper proposes a novel image expansion method based on ellipse extrapolation; it enhances extrapolation accuracy by deriving direct estimates of the observed range points distributed in the data space. Numerical validation shows that the proposed method accurately extrapolates part of the target boundary, even if an extremely small region of the target boundary is obtained by RPM.

In this paper, we introduce an approach of service adaptation for behavior mismatching services using pushdown model checking. This approach uses pushdown systems as model of adaptors so that capturing non-regular behavior in service interactions is possible. Also, the use of pushdown model checking integrates adaptation and verification. This guarantees that an adaptor generated by our approach not only solves behavior mismatches but also satisfies usual verification properties if specified. Unlike conventional approaches, we do not count on specifications of adaptor contracts but take only information from behavior interfaces of services and perform fully automated adaptor generation. Three requirements relating to behavior mismatches, unbounded messages, and branchings are retrieved from behavior interfaces and used to build LTL properties for pushdown model checking. Properties for unbounded messages, i.e., messages sent and received arbitrary multiple times, are especially addressed since it characterizes non-regular behavior in service composition. This paper also shows some experimental results from a prototype tool and provides directions for building BPEL adaptors from behavior interface of generated adaptor. The results show that our approach does solve behavior mismatches and successfully capture non-regular behavior in service composition under the scale of real service applications.

Visual degradation is usually introduced during 3D mesh simplification. The main issue in mesh simplification is to maximize the simplification ratio while minimizing the visual degradation. Therefore, effective and objective evaluation of the visual degradation is essential in order to select the simplification ratio. Some objective geometric and subjective perceptual metrics have been proposed. However, few objective metrics have taken human visual characteristics into consideration. To evaluate the visual degradation introduced by mesh simplification for a 3D triangular object, we integrate the structural degradation with mesh saliency and propose a new objective and multi-scale evaluation metric named Global Perceptual Structural Degradation (GPSD). The proper selection of the simplification ratio under a given distance-to-viewpoint is also discussed in this paper. The accuracy and validity of the proposed metric have been demonstrated through subjective experiments. The experimental results confirm that the GPSD metric shows better 3D model-based multi-scale perceptual evaluation capability.

We propose a new coding scheme for lossless bit rate reduction of the MPEG Surround module in unified speech and audio coding (USAC). The proposed scheme is based on context-adaptive arithmetic coding for efficient bit stream composition of spatial parameters. Experiments show that it achieves the significant lossless bit reduction of 9.93% to 12.14% for spatial parameters and 8.64% to 8.96% for the overall MPEG Surround bit streams compared to the original scheme. The proposed scheme, which is not currently included in USAC, can be used for the improved coding efficiency of MPEG Surround in USAC, where the saved bits can be utilized by the other modules in USAC.

The authors have proposed Multi-Input Multi-Output (MIMO)-Constant Envelope Modulation, (MIMO-CEM), as a power and complexity efficient alternative to MIMO-OFDM, suitable for wireless backhaul networks in which relay nodes are fixed in their positions. One of the major problems hindering the real application of MIMO-CEM is to estimate MIMO channel characteristics. MIMO-CEM is based upon two contrary schemes; one is nonlinear equalization such as maximum likelihood sequence estimator, which needs accurate channel information to replicate the received signal passing through it. The other is a low resolution analog-to-digital converter (ADC), e.g., 1-bit in the default operation that removes the received signal amplitude fluctuation. In this paper, as a solution to the channel estimation problem in MIMO-CEM with low resolution ADC receiver, we propose an adaptive MIMO-CEM channel estimation scheme where iterative adaptive channel estimation is carried out to minimize the error between the received preamble signal and the replicated one. We also prove that Code Division Multiplexing (CDM) preamble transmission is effective in estimating MIMO channel parameters in the presence of large quantization noise. Computer simulation results show that MIMO-CEM with the proposed channel estimator using CDM preambles achieves identical BER performance to that with the ideal channel estimation even in presence of severe quantization noise caused by a low resolution ADC.

In this paper, we consider the problem of waveform optimization for multi-input multi-output (MIMO) radar in the presence of signal-dependent noise. A novel diagonal loading (DL) based method is proposed to optimize the waveform covariance matrix (WCM) for minimizing the Cramer-Rao bound (CRB) which improves the performance of parameter estimation. The resulting nonlinear optimization problem is solved by resorting to a convex relaxation that belongs to the semidefinite programming (SDP) class. An optimal solution to the initial problem is then constructed through a suitable approximation to an optimal solution of the relaxed one (in a least squares (LS) sense). Numerical results show that the performance of parameter estimation can be improved considerably by the proposed method compared to uncorrelated waveforms.

In the work we introduce novel approach to remote sensing from space for the estimation of sea wave heights with a spaceborne high precision two-frequency radar altimeter with nadir synthesis antenna aperture. Experiments show considerable reduction of the decorrelation factor of the correlation coefficient and so significant enhancement of the sensitivity of the altimeter for the estimation for the sea wave status.

This paper is about a new efficient method for the implementation of a Block Proportionate Normalized Least Mean Square (BPNLMS++) adaptive filter using the Fermat Number Transform (FNT) and its inverse (IFNT). These transforms present advantages compared to Fast Fourier Transform (FFT) and the inverse (IFFT). An efficient state space method for implementing the FNT over rectangular windows is used in the cases where there is a large overlap between the consecutive input signals. This is called Generalized Sliding Fermat Number Transform (GSFNT) and is useful for reducing the computational complexity of finite ring convolvers and correlators. In this contribution, we propose, as a first objective, an efficient state algorithm with the purpose of reducing the complexity of IFNT. This algorithm, called Inverse Generalized Sliding Fermat Number Transform (IGSFNT), uses the technique of Generalized Sliding associated to matricial calculation in the Galois Field. The second objective is to realize an implementation of the BPNLMS++ adaptive filter using GSFNT and IGSFNT, which can significantly reduce the computation complexity of the filter implantation on digital signal processors.

We present a new framework of the data-reusing (DR) adaptive algorithms by incorporating a constraint on noise, referred to as a noise constraint. The motivation behind this work is that the use of the statistical knowledge of the channel noise can contribute toward improving the convergence performance of an adaptive filter in identifying a noisy linear finite impulse response (FIR) channel. By incorporating the noise constraint into the cost function of the DR adaptive algorithms, the noise constrained DR (NC-DR) adaptive algorithms are derived. Experimental results clearly indicate their superior performance over the conventional DR ones.

The Liquid-crystal display (LCD) overdrive technique has been utilized to reduce motion blur on a display via a reduction in the response time. However, to measure the variation of the pixel amplitudes, it is necessary to store the previous frame using a large frame memory. To downscale the frame memory, block truncation coding (BTC) is commonly employed due to the simplicity of its implementation, even if some visual artifacts may occur for image blocks with high frequency components. In this paper, we present a multimode-multilevel BTC (MBTC) technique that improves performance while maintaining simplicity. To improve the visual quality, we uniquely determine the quantization level and coding mode of each block according to the distribution of the luminance and chrominance amplitudes. For a compression ratio of 6:1, the proposed method demonstrates higher coding efficiency and overdrive performance by up to 3.81 dB in the PSNR compared to other methods.

The traffic adaptive 2-level active period control has been proposed as a traffic adaptation mechanism to handle temporal and spatial (geographical) traffic fluctuations in cluster-based wireless sensor networks (WSNs) employing IEEE802.15.4 medium access control (MAC). This paper proposes a traffic adaptive distributed backoff control mechanism for cluster-based WSNs with the traffic adaptive 2-level active period control to enhance the system performance, especially transmission performance. The proposed mechanism autonomously adjusts the starting time of the backoff procedure for channel accesses in the contention access period (CAP) specified by the IEEE802.15.4 MAC, and then distributes the channel access timing over a wide range within the CAP, which can mitigate channel access congestion. The results of computer simulations show that the proposed mechanism can improve the transmission delay performance while keeping the enhancement in throughput and energy consumption at the cluster-based WSNs under non-uniform traffic environments.

The proliferation of many-core architectures has led to the explosive development of parallel applications using programming models, such as OpenMP, TBB, and Cilk/Cilk++. With increasing number of cores, however, it becomes even harder to efficiently schedule parallel applications on these resources since current many-core runtime systems still lack effective mechanisms to support collaborative scheduling of these applications. In this paper, we study feedback-driven adaptive scheduling based on work stealing, which provides an efficient solution for concurrently executing a set of applications on many-core systems. To dynamically estimate the number of cores desired by each application, a stable feedback-driven adaptive algorithm, called SAWS, is proposed using active workers and the length of active deques, which well captures the runtime characteristics of the applications. Furthermore, a prototype system is built by extending the Cilk runtime system, and the experimental results, which are obtained on a Sun Fire server, show that SAWS has more advantages for scheduling concurrent parallel applications. Specifically, compared with existing algorithms A-Steal and WS-EQUI, SAWS improves the performances by up to 12.43% and 21.32% with respect to mean response time respectively, and 25.78% and 46.98% with respect to processor utilization, respectively.

As process technology is scaled down, a typical system on a chip (SoC) becomes denser. In scaled process technology, process variation becomes greater and increasingly affects the SoC circuits. Moreover, the process variation strongly affects network-on-chips (NoCs) that have a synchronous network across the chip. Therefore, its network frequency is degraded. We propose a process-variation-adaptive NoC with a variation-adaptive variable-cycle router (VAVCR). The proposed VAVCR can configure its cycle latency adaptively on a processor core basis, corresponding to the process variation. It can increase the network frequency, which is limited by the process variation in a conventional router. Furthermore, we propose a variable-cycle pipeline adaptive routing (VCPAR) method with VAVCR; the proposed VCPAR can reduce packet latency and has tolerance to network congestion. The total execution time reduction of the proposed VAVCR with VCPAR is 15.7%, on average, for five task graphs.

Fidelity Range Extension (FRExt) (i.e. High Profile) was added to the H.264/AVC recommendation in the second version. One of the features included in FRExt is the Adaptive Block-size Transform (ABT). In order to conform to the FRExt, a Fractional Motion Estimation (FME) architecture is proposed to support the 88/44 adaptive Hadamard Transform (88/44 AHT). The 88/44 AHT circuit contributes to higher throughput and encoding performance. In order to increase the utilization of SATD (Sum of Absolute Transformed Difference) Generator (SG) in unit time, the proposed architecture employs two 8-pel interpolators (IP) to time-share one SG. These two IPs can work in turn to provide the available data continuously to the SG, which increases the data throughput and significantly reduces the cycles that are needed to process one Macroblock. Furthermore, this architecture also exploits the linear feature of Hadamard Transform to generate the quarter-pel SATD. This method could help to shorten the long datapath in the second-step of two-iteration FME algorithm. Finally, experimental results show that this architecture could be used in the applications requiring different performances by adjusting the supported modes and operation frequency. It can support the real-time encoding of the seven-mode 4 K2 K@24 fps or six-mode 4 K2 K@30 fps video sequences.

An adaptive rate controller (ARC) based on an adaptive neural fuzzy inference system (ANFIS) is designed to autonomously adjust the data rate of a mobile heterogeneous network to adapt to the changing traffic load and the user speed for multimedia call services. The effect of user speed on the handoff rate is considered. Through simulations, it has been demonstrated that the ANFIS-ARC is able to maintain new call blocking probability and handoff failure probability of the mobile heterogeneous network below a prescribed low level over different user speeds and new call origination rates while optimizing the average throughput. It has also been shown that the mobile cognitive wireless network with the proposed CS-ANFIS-ARC protocol can support more traffic load than neural fuzzy call-admission and rate controller (NFCRC) protocol.