This letter investigates the circularity of fractional Fourier transform (FRFT) coefficients containing noise only, and proves that all coefficients coming from white Gaussian noise are circular via the discrete FRFT. In order to use the spectrum kurtosis (SK) as a Gaussian test to check if linear frequency modulation (LFM) signals are present in a set of FRFT points, the effect of the noncircularity of Gaussian variables upon the SK of FRFT coefficients is studied. The SK of the α th-order FRFT coefficients for LFM signals embedded in a white Gaussian noise is also derived in this letter. Finally the signal detection algorithm based on FRFT and SK is proposed. The effectiveness and robustness of this algorithm are evaluated via simulations under lower SNR and weaker components.

We introduce the distributed estimation of a random vector signal in wireless sensor networks that follow coherent multiple access channel model. We adopt the linear minimum mean squared error fusion rule. The problem of interest is to design linear coding matrices for those sensors in the network so as to minimize mean squared error of the estimated vector signal under a total power constraint. We show that the problem can be formulated as a convex optimization problem and we obtain closed form expressions of the coding matrices. Numerical results are used to illustrate the performance of the proposed method.

In this paper, a Compensatory Neuro-Fuzzy Network (CNFN) for nonlinear system control is proposed. The compensatory fuzzy reasoning method is using adaptive fuzzy operations of neural fuzzy network that can make the fuzzy logic system more adaptive and effective. An on-line learning algorithm is proposed to automatically construct the CNFN. They are created and adapted as on-line learning proceeds via simultaneous structure and parameter learning. The structure learning is based on the fuzzy similarity measure and the parameter learning is based on backpropagation algorithm. The advantages of the proposed learning algorithm are that it converges quickly and the obtained fuzzy rules are more precise. The performance of CNFN compares excellently with other various exiting model.

This paper introduces a texture analysis mechanism utilizing multiresolution technique to reduce false motion detection and hence thoroughly improve the interpolation results for high-quality deinterlacing. Conventional motion-adaptive deinterlacing algorithm selects from inter-field and intra-field interpolations according to motion. Accurate determination of motion information is essential for this purpose. Fine textures, having high local pixel variation, tend to cause false detection of motion. Based on hierarchical wavelet analysis, this algorithm provides much better perceptual visual quality and considerably higher PSNR than other motion adaptive deinterlacers as shown. In addition, a recursive 3-field motion detection algorithm is also proposed to achieve better performance than the traditional 2-field motion detection algorithm with little memory overhead.

The stopping distance and stopping redundancy of a linear code are important concepts in the analysis of the performance and complexity of the code under iterative decoding on a binary erasure channel. In this paper, we studied the stopping distance and stopping redundancy of Finite Geometry LDPC (FG-LDPC) codes, and derived an upper bound of the stopping redundancy of FG-LDPC codes. It is shown from the bound that the stopping redundancy of the codes is less than the code length. Therefore, FG-LDPC codes give a good trade-off between the performance and complexity and hence are a very good choice for practical applications.

In this paper, novel hardware sharing architectures are proposed for realizations of fast 44 and 88 forward/inverse integer transforms in H.264/AVC applications. Based on matrix factorizations, the cost-effective architectures for fast one-dimensional (1-D) 44 and 88 forward/inverse integer transforms can be derived through the Kronecker and direct sum operations. By applying the concept of hardware sharing, the proposed hardware schemes for fast integer transforms need a smaller number of shifters and adders than the direct realization architecture, where the direct architecture just implements the individual 44 and individual 88 integer transforms independently. With low hardware cost and regular modularity, the proposed hardware sharing architectures can process up to 125 MHz with the cost-effective area and are suitable for VLSI implementations to accomplish the H.264/AVC signal processing.

This paper describes a novel harmonic-based robust voice activity detection (H-RVAD) method with harmonic spectral local peak (HSLP) feature. HSLP is extracted by spectral amplitude analysis between the adjacent formants, and such characteristic can be used to identify and verify audio stream containing meaningful human speech accurately in low SNR environment. And, an enhanced low SNR noisy speech recognition system framework with wakeup module, speech recognition module and confirmation module is proposed. Users can determine or reject the system feedback while a recognition result was given in the framework, to prevent any chance that the voiced noise misleads the recognition result. The H-RVAD method is evaluated by the AURORA2 corpus in eight types of noise and three SNR levels and increased overall average performance from 4% to 20%. In home noise, the performance of H-RVAD method can be performed from 4% to 14% sentence recognition rate in average.

Subcircuits designed for integrated silicon DCS1800/ PCS1900 base station receivers have been reconfigured into hybrid and single-chip Doppler radar transceivers. Radar chips have been fully integrated in 0.25 µm silicon CMOS and BiCMOS processes. These chips have been used to monitor heart and respiration activity without contact, and they have successfully detected heartbeat and respiration rate up to 1 m from the subject. This monitoring device may be useful in home monitoring, continuous monitoring, and physiological research.

In this paper, we propose a simplified transform-based and variable-rate vocoder, which is evolved from the code-excited linear prediction (CELP) coding structure. With pre-emphasis and de-emphasis filters, the transformed-based CELP vocoder incorporates a long-term predictor, a discrete cosine transform (DCT), and pre-filters and postfilters for achieving perceptually weighted quantization. The proposed transform-based vocoder requires less computational complexity with slightly worse quality than the CELP coders. Furthermore, the proposed DCT-based coding structure easily figured with additional DCT coefficients could simultaneously offer low, middle, and high bit rates to adapt the variation of bandwidth for modern Internet or wireless communications.

There are a great amount of various resources described in many different ways for service oriented grid environment, while traditional grid resource discovery methods could not fit more complex future grid system. Therefore, this paper proposes a novel grid resource discovery method based on association rule hypergraph partitioning algorithm which analyzes user behavior in history transaction records to provide personality service for user. And this resource discovery method gives a new way to improve resource retrieval and management in grid research.

This paper proposes a new admission control strategy for ATM networks, which is based on the simulation approach and regression results. Instead of using many traffic descriptors, in our strategy only numbers of connections of different types are needed in performing admission control. The strategy is evaluated from different points of view, real-time, safety, policing and its efficiency which is referred as allowed utilized bandwidth. Since the admission criteria is developed in a form of regression model, the computation of performance for accepting a new connection is quick and easy. Using the confidence region in statistics to represent the admission criteria, a conservative estimation of performance can be achieved. Besides, this strategy is quite independent, thus can be compatible with most policing functions. Finally, its bandwidth utilization is found to be above 0.54. However, the success of this strategy still depends on the reality of input traffic model. Whenever the traffic can be clearly described, the proposed strategy can be easily and precisely applied. Therefore, we also build a traffic model for different type of traffic including constant-bit-rate (CBR), variable-bit-rate (VBR) and bursty traffic. The application of the proposed strategy to different multiplexing schemes, like priority queues and polling system, etc., should be further studied. Considering different level of performance requirement for different type of traffic, which should aid the bandwidth utilization of this strategy, is also an interesting research issue.

Using linear programming (LP) decoding based on alternating direction method of multipliers (ADMM) for low-density parity-check (LDPC) codes shows lower complexity than the original LP decoding. However, the development of the ADMM-LP decoding algorithm could still be limited by the computational complexity of Euclidean projections onto parity check polytope. In this paper, we proposed a bisection method iterative algorithm (BMIA) for projection onto parity check polytope avoiding sorting operation and the complexity is linear. In addition, the convergence of the proposed algorithm is more than three times as fast as the existing algorithm, which can even be 10 times in the case of high input dimension.

A novel approach of robust adaptive beamforming (RABF) is presented in this paper, aiming at robustness against both finite-sample effects and steering vector mismatches. It belongs to the class of diagonal loading approaches with the loading level determined based on worst-case performance optimization. The proposed approach, however, is distinguished by two points. (1) It takes finite-sample effects into account and applies worst-case performance optimization to not only the constraints, but also the objective of the constrained quadratic equation, for which it is referred to as joint worst-case RABF (JW-RABF). (2) It suggests a simple closed-form solution to the optimal loading after some approximations, revealing how different factors affect the loading. Compared with many existing methods in this field, the proposed one achieves better robustness in the case of small sample data size as well as steering vector mismatches. Moreover, it is less computationally demanding for presenting a simple closed-form solution to the optimal loading. Numerical examples confirm the effectiveness of the proposed approach.

We obtain an upper bound for the maximum aperiodic and odd correlations of the recently derived p-ary sequences from Galois rings [1]. We use the upper bound on hybrid sums over Galois rings [5], the Vinogradov method [4] and the methods of [5] and [6].

In multimedia communication, due to the limited computational capability of the personal information machine, a coder with low computational complexity is needed to integrate services from several media sources. This paper presents two efficient candidate schemes to simplify the most computationally demanding operation, the excitation codebook search procedure. For fast adaptive codebook search, we propose an algorithm that uses residual signals to predict the candidate gain-vectors of the adaptive codebook. For the fixed codebook, we propose a fast search algorithm using an energy function to predict the candidate pulses, and we redesign the codebook structure to twin multi-track positions architecture. Overall simulation results indicate that the average perceptual evaluation of speech quality (PESQ) score is degraded slightly, by 0.049, and our proposed methods can reduce total computational complexity by about 67% relative to the original G.723.1 encoder computation load, and with perceptually negligible degradation. Objective and subjective evaluations verify that the more efficient candidate schemes we propose can provide speech quality comparable to that using the original coder approach.

The long-term predictor (LTP) can effectively reduce the redundancy of the quasi-periodicity of speech signals. Traditional pitch predictors in linear predictive coding systems are developed by assuming that pitch gains are constant in each processing subframe. In this paper, we introduce a time-varied prediction gain to achieve a better representation of speech periodicity than the traditional approach. Due to the non-stationary variation of speech, the proposed LTP method can trace the detailed fluctuation of speech amplitude in both transient and stationary periods. Simulation results show that the proposed first-order varied-gain pitch predictor obtains a near speech quality as the fifth-order pitch predictor of the G.723.1 coder does; however, the former requires much lower computation than the latter.

Since ISAR is widely applied in many occasions and provides high resolution images of the target, ISAR countermeasures are attracting more and more attention. Most of the present methods of deception jamming are not suitable for engineering realization due to the heavy computation load or the large calculation delay. Deception jamming against ISAR requires large computation resource and real-time performance algorithms. Many studies on false target jamming assume that the jammer is able to receive the target echo or transmit the jamming signal to the real target, which is sometimes not possible. How to impose the target property onto the intercepted radar signal is critical to a deception jammer. This paper proposes a jamming algorithm based on parallel convolution and one-bit quantization. The algorithm is able to produce a single false target on ISAR image by the jammer itself. The requirement for computation resource is within the capabilities of current digital signal processors such as FPGA or DSP. The method processes the samples of radar signal in parallel and generates the jamming signal at the rate of ADC data, solving the problem that the real-time performance is not satisfied when the input data rate for convolution is far higher than the clock frequency of FPGA. In order to reduce the computation load of convolution, one-bit quantization is utilized. The complex multiplication is implemented by logical resources, which significantly reduces the consumption of FPGA multipliers. The parallel convolution jamming signal, whose date rate exceeds the FPGA clock rate, is introduced and analyzed in detail. In theory, the bandwidth of jamming signal can be half of the sampling frequency of high speed ADC, making the proposed jamming algorithm able to counter ultra-wideband ISAR signals. The performance and validity of the proposed method are verified by simulations. This jamming method is real-time and capable of producing a false target of large size at the low cost of FPGA device.

A new algorithm, termed reduced-order Root-MUSIC, for high resolution direction finding is proposed. It requires finding all the roots of a polynomial with an order equaling twice the number of propagating signals. Some Monte Carlo simulations are used to test the effectiveness of the proposed algorithm.

The UCHT (Unified Complex Hadamard Transform) has been proposed as a new family of spreading sequences for DS-SSMA systems recently. In this Letter, the periodic autocorrelation (PAC) properties of the Unified Complex Hadamard Transform (UCHT) sequences are analyzed. Upper bounds for the out-of-phase PAC are derived for two groups of the UCHT sequences, namely the HSP-UCHT and the NHSP-UCHT sequences (the later is a more general representation of the well-known Walsh-Hadamard (WH) sequences). A comparison of the two bounds is performed. It turns out that the HSP-UCHT sequences have a lower upper bound for the out-of-phase PAC. This makes the HSP-UCHT sequences more effective than the WH sequences in combating multipath effect for DS-SSMA systems.

Detecting anomalies, such as network failure or intentional attack in Internet, is a vital but challenging task. Although numerous techniques have been developed based on Internet traffic, detecting anomalies from the perspective of Internet topology structure is going to be possible because the anomaly detection of structured datasets based on complex network theory has become a focus of attention recently. In this paper, an anomaly detection method for the large-scale Internet topology is proposed to detect local structure crashes caused by the cascading failure. In order to quantify the dynamic changes of Internet topology, the network path changes coefficient (NPCC) is put forward which highlights the Internet abnormal state after it is attacked continuously. Furthermore, inspired by Fibonacci Sequence, we proposed the decision function that can determine whether the Internet is abnormal or not. That is the current Internet is abnormal if its NPCC is out of the normal domain calculated using the previous k NPCCs of Internet topology. Finally the new Internet anomaly detection method is tested against the topology data of three Internet anomaly events. The results show that the detection accuracy of all events are over 97%, the detection precision for three events are 90.24%, 83.33% and 66.67%, when k=36. According to the experimental values of index F1, larger values of k offer better detection performance. Meanwhile, our method has better performance for the anomaly behaviors caused by network failure than those caused by intentional attack. Compared with traditional anomaly detection methods, our work is more simple and powerful for the government or organization in items of detecting large-scale abnormal events.