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In this paper, we compared several software defined radio platforms and proposed the general architecture of GPP (General Purpose Processor)-based soft base stations. Then, we studied on the scheme design of resource allocation and algorithm optimization in soft base station implementation. Finally, as an instance of our study above, a prototype of GPP-based soft base station referring to 3GPP LTE was realized and evaluated. With its evaluated performance, GPP's capability on processing high speed wireless signal flow was verified, which showed us a promising prospect for GPP-based soft base stations in the next generation wireless systems. [ABSTRACT FROM PUBLISHER]

Time series that composed of disperse observation like climatic time series have nonlinear and nonstationary features. Because of the superiority of Support Vector Machine in solving nonlinear problem and the advantage of Empirical Mode Decomposition in handling nonstationary signal, this paper combined the two methods in the research on chaotic time series prediction, and applied it to the seasonal precipitation forecast in Guangxi Zhuang Autonomous Region. Apart from this, this paper compares this result with RBF neural network algorithm and Support Vector Machine algorithm neither with the Empirical Mode Decomposition algorithm. Results show that relative to the directly predict methods, algorithm in this paper has the higher precision in prediction and better generalization ability. [ABSTRACT FROM PUBLISHER]

Codebook design plays a crucial role in the performance of speech signal processing systems based on vector quantization, such as speech codec, speech recognition and so on. LBG algorithm is one of the most effective methods to generate codebooks and is commonly used in multidimensional signal processing. But it has the problem of initialize codebook design. The paper proposed adopting ant colony clustering algorithm to design codebook. But the clustering number of ant colony clustering algorithm is automatically formed, it may not be exactly equal to the codebook size. So the paper put forward solving measures on the basis of nearest neighbor criterion or decomposition method. The codebook design procedures with ant colony clustering algorithm is given in the paper detailedly. Using the proposed method, the paper designed vector quantizer' codebooks of adaptive multi-rate wideband (AMR-WB) speech codec. Simulation results showed that the decoding speech qualities of AMR-WB speech codec were not almost degraded adopting newly designed codebooks. The transparent quantization effect of vector quantization was obtained. [ABSTRACT FROM PUBLISHER]

A critical enabling technology for electrified vehicles and renewable energy resources is battery energy storage. Advanced battery systems represent a promising technology for these applications, however their dynamics are governed by relatively complex electrochemical phenomena whose parameters degrade over time and vary across material design. Moreover, limited sensing and actuation exists to monitor and control the internal state of these systems. As such, battery management systems require advanced identification, estimation, and control algorithms. In this paper we examine state-of-health (SOH) estimation, framed as a parameter identification problem for parabolic PDEs and nonlinearly parameterized output functions. Specifically, we utilize the swapping identification method for unknown parameters in the diffusion partial differential equation (PDE). A nonlinear least squares method is applied to the output function to identify its unknown parameters. These identification algorithms are synthesized from the single particle model (SPM). In a companion paper we examine a new battery state-of-charge (SOC) estimation algorithm based upon the backstepping method for PDEs. [ABSTRACT FROM PUBLISHER]

This paper presents a real life localization scenario. A small scale network based on Xbee transceivers is used in order to verify positioning algorithms. The localization method is RSSI based; the algorithms used for localization are maximum likelihood estimation, weighted centroid localization, Malguki spring, modified multidimensional scaling and iterative trilateration. The algorithm performance is evaluated based on the positioning error and on their computational time. An optimization criterion is proposed. [ABSTRACT FROM PUBLISHER]

This paper proposes an algorithm to enable a secondary user in a cognitive radio network to be aware of whether each primary transmitter is active or passive, namely network state estimation. The number of transmitters is unknown apriori. A secondary user needs to perform a multiple-hypothesis testing, which can be achieved via clustering the received observations such that observations sharing the same cluster are declared to have been generated from the same hypothesis. However, most of the clustering algorithms assume that data is available offline as a batch. We have earlier proposed the LOC algorithm, a Large-scale Online hierarchical Clustering algorithm for unsupervised sequential numerical data. Unlike most of clustering algorithms, hierarchical algorithms do not assume the number of clusters to be known apriori. In this paper, the LOC algorithm is applied in the context of multiple-hypothesis testing to enable each secondary user to track the network state. The received samples at a secondary user are fed to a proposed filter then to the LOC algorithm to obtain a hierarchical tree. We study the choice of an adequate cutting level and evaluate the spectrum sensing performance. We report a 93% average probability of correctly declaring a true hypothesis in a scenario where number of true hypotheses in the network is five. [ABSTRACT FROM PUBLISHER]

Edges Detection is a significant task in many fields of application. Some signal features can be reflected by their discontinuous structure. In general, a mathematical signal transform carries more features than a raw signal. Thus, for analyzing an intercepted signal in a totally blind environment, one can benefit from the information the signal spectrum offers through the Fourier Transform. After sensing the spectrum, the characteristics extraction method must be applied. In this paper, the Wavelet Transform is used, as it exhibits excellent ability to analyze a signal. Also introduced in this paper is an improved algorithm that automatically identifies bandwidth signal boundaries in a determined frequency range. The proposed algorithm is based on the local maxima of the Wavelet Transform modulus. In blind conditions and in a noisy environment, this approach grants good performance. [ABSTRACT FROM PUBLISHER]

Power consumption has become an important issue in embedded system. Reducing the total number of signal transitions during each bus cycle has great effect on power consumption. In this paper, we proposed an instruction scheduling method which is based on the Particle Swarm Optimization (PSO) Algorithm to reduce the signal transitions. We formulate the low-power instruction scheduling problem to the constrained discrete PSO problem. And then we modify the PSO solution to satisfy the path constrained and find the better instruction schedule with litter signal transitions. For more effectively, we also improve the velocity updating formula to get the better result. The main contribution of our paper is that we use the improved PSO algorithm to find the better instructions sequence for low power quickly. [ABSTRACT FROM PUBLISHER]

This paper presents architecture of CORDIC, embedded with a scaling unit that has only minimal number of adders and shifters. It can be implemented in rotation mode as well as vectoring mode. The purpose of the design is to get a scaling free CORDIC unit preserving the design of original algorithm. The proposed design has a considerable reduction in hardware when compared with other scaling free architectures. The analysis of error for different word lengths and different input ranges for fixed word length gives a better choice to choose the parameters. The error in rotation mode for 16 bit data path, obtained for ordinate equivalent input is 0.073% and for abscissa equivalent input is 0.067%. The proposed design implemented in Xilinx XC3S500E-4FG320 FPGA, fabricated in 90 nm process technology, consumes 503 slices and 984 4-input look up tables (LUTs). The proposed design has a maximum frequency of operation of 75.593 MHz and a slice delay product of 104.645. [ABSTRACT FROM PUBLISHER]

This paper proposes an optimal orthogonal overcomplete kernel design for sparse representation such that the sum of the L1 norms of a set of transformed vectors is minimized. When there is only one training vector in the set, both the optimal transformed vector and the optimal orthogonal kernel are derived analytically. When there is more than one training vector in the sets, this optimization problem is difficult to solve due to the orthogonal quadratic constraint. To address this difficulty, the paper proposes to convert the quadratic constrained optimization problem to an optimal rotational angle design problem. A set of vectors of rotational angles are initialized and the best converged vector of the rotational angles among the set is taken as the nearly globally optimal solution of the problem. Simulation results show that the proposed methodology is very effective and efficient. [ABSTRACT FROM PUBLISHER]

HMM is one of the most popular methods to model sequential signals and plays a significant role in the field of off-line handwritten Arabic word recognition research. However, the structure of an HMM including the number of states has to be determined initially and can hardly be updated during the training process. A novel analytic algorithm based on the information entropy of states in an HMM to optimize the number of states will be proposed in this paper. Information entropy is defined as an evaluation criterion of the activity of a state. According to principle of maximum entropy, states with minor information entropy do not possess so enough capability to represent actual observations that they should be deleted. Experiments on IFN/ENIT database show that the algorithm in this paper can bring approximately 3%–6% increase to correct recognition rate from the best performance of system with constant states. [ABSTRACT FROM PUBLISHER]

GTRI is developing an 8-channel X-band experimental radar for a small unmanned aircraft system (SUAS) for adaptive multi-channel, MIMO, and waveform diversity test bed studies. New adaptive algorithms, one of which is covered in this paper, are also part of the test bed. Estimation of the statistical covariance matrix forms a central role in radar detection and adaptive beamforming algorithm. For example, the optimal (adaptive) linear combiner (beamformer) weights for a radar sensor array are expressed in terms of the inverse of the multi-channel (MC) covariance matrix for MIMO problems. Rather than form an estimate of the covariance matrix directly from the available data and inverting (sample matrix inversion [SMI]), an alternative direct estimate of the inverse may be obtained by forming parametric MC linear prediction estimates and then expressing the inverse in terms of these parametric MC estimates. The resulting parametric inverse estimate will be more accurate than inverting the estimate of the covariance matrix, leading to greatly improved detection performance over conventional methods of covariance estimation and inversion. This paper reveals the structure of the inverse of the covariance matrix for one parametric technique. The inverse structure involves products of triangular block MC Toeplitz matrices, which leads to a fast computational solution. Performance improvements over classical sample covariance matrix estimation are illustrated. [ABSTRACT FROM PUBLISHER]

Formal method is an important approach for construction of the trustworthy software. Koch curve is one of the typical fractals. Employing PAR method and the strategy of developing loop invariant, this paper develops non-recursive algorithmic program of L system based Koch curve and verifies the program formally. This paper aims at non-recursive algorithms directly", "and achieves loop invariant of L system based Koch curve with readable", "efficient and reliable non-recursive algorithm finally. The paper contributes to developing non-recursive algorithm using formal method and new strategy of developing loop invariant. [ABSTRACT FROM PUBLISHER]

The ionosphere plays an important role in high-frequency radio communication. An ionogram, as an effective way to get useful information of ionosphere, should be analyzed correctly and effectively. Unfortunately, the ionograms are generally distorted by background noise and strong signals interferences from other transmitters. In this paper, two techniques are proposed to obtain high-quality ionograms by reducing those interferences. The proposed techniques contain radio interference mitigation and modified mean filtering. These techniques have been well proved by data from ionosonde and perfect result could be seen from the comparison between the processed data and the unprocessed data. After getting the clearer ionograms, an algorithm is proposed for extracting main traces from the ionograms which is based on image recognition. Also, the performance of this algorithm is tested by data from ionosonde and the result is perfectly well. The test data were obtained from the ionosonde operating at Dulan. The proposed algorithms in this paper should be useful for further analysis of ionograms. [ABSTRACT FROM PUBLISHER]

In this paper, a new fast convergence adaptive algorithm with variable step size is proposed for UWB asynchronous location. This new proposed algorithm is derived on the distance between the targets and measurement point, which adjusts adaptively the step size with the change of gradient descent. Numerical experiment shows that algorithm is stable in the effective rectangular area and the positioning error may be up to millimetres order of magnitude. Meanwhile the proposed algorithm decreases a computation as well as minimizes the processing delaying compared with the fixed step size, furthermore, the proposed algorithm overcomes the fluctuating that closed to the targets. Results of simulation show that the proposed method has better location accuracy, high convergence speed and low computation than the related decentralized approaches described previous. [ABSTRACT FROM PUBLISHER]

In present day to day life, effective data hiding methods are needed due to attack made on data communication. This paper presents the technique for the above requirement. In this proposed method, secret message in the form of audio file is embedded within another carrier audio file (. wav). In the transmitter end the output will be similar to the carrier with a secret message embedded inside. The hacker will be blinded by the transmitted signal. At the receiver end the original message can be retrieved without any loss. The entire proposed system is simulated and their corresponding waveforms prove the effectiveness of this method. [ABSTRACT FROM PUBLISHER]

The Fast Fourier Transform (FFT) is one of the rudimentary operations in field of digital signal and image processing. Some of the very vital applications of the fast fourier transform include Signal analysis, Sound filtering, Data compression, Partial differential equations, Multiplication of large integers, Image filtering etc. Fast Fourier transform (FFT) is an efficient implementation of the discrete Fourier transform (DFT). This paper concentrates on the development of the Fast Fourier Transform (FFT), based on Decimation-In-Time (DIT) domain, Radix-2 algorithm, this paper uses VHDL as a design entity, and their Synthesis by Xilinx Synthesis Tool on Vertex kit has been done. The input of Fast Fourier transform has been given by a PS2 KEYBOARD using a test bench and output has been displayed using the waveforms on the Xilinx Design Suite 12.1. The synthesis results show that the computation for calculating the 32-point Fast Fourier transform is efficient in terms of speed. [ABSTRACT FROM PUBLISHER]

In the design of WSN protocols the harsh environments and the existence of threats demand more careful security considerations. Cryptography is the art of achieving security by encoding messages to keep the information non-readable. Cryptography actually is a mathematical scrambling and unscrambling of data to achieve, Confidentiality (Readable only to sender and receiver), Integrity (not modified by any one to integrity of message), Availability (sender can't refuse the claim of not sending), and Entity authentication. While sending or receiving the data, special keys are used to encrypt/decrypt data to verify the original contents. This key makes the process of cryptography secure. There are two approaches for the generation of encryption/decryption keys i.e. software and hardware. The purpose of this paper is to generate encryption key using hardware technique. To design electronics circuit basic concept is derived from encoding and decoding of data via four different channels at different times. Selections of channels will be guided by timer and decoder circuits. As in the defense there are different hierarchy levels and they need secure communication. The purpose of this paper is to generate the key codes using electronic circuits to make the data more secure. The technique used in this paper is more secure then discussed in prior papers. [ABSTRACT FROM PUBLISHER]

In video communications, the compressed video stream is very likely to be corrupted by channel errors. Recently, many error concealment algorithms have been proposed in order to combat channel errors. In this paper, we have combined two state-of-the-art motion vector recovery algorithms into a even better algorithm; we have modified the hybrid motion vector extrapolation (HMVE) algorithm and further improved the accuracy of the derived motion vectors by pixel-based MV correction using neighboring received MVs. Experimental results show that this method is able to provide better performance than existing algorithms subjectively and objectively. [ABSTRACT FROM PUBLISHER]

In this paper, a new deinterlacing algorithm which uses an adaptive bilateral filter with local intensity histogram screening and generalized fuzzy operation is proposed. Adaptive bilateral filter with local intensity histogram screening can effectively prevent producing noise magnification or over-sharpening artifacts. Generalized fuzzy operation can enhance image local contrast and prevent involving noise. Experimental results demonstrate the proposed algorithm outperforms the mostly popular algorithms. [ABSTRACT FROM PUBLISHER]

Vehicular Ad-hoc Networks (VANETs) are distributed, self-organizing communication networks built up by moving vehicles, and are thus characterized by a very high node mobility and limited degrees of freedom in the mobility patterns. Such particular features often make standard networking protocols inefficient or unusable in VANETs. In the past, the wireless networking community relied on simple models such as random waypoint . However, this model has found to be too simplistic although very useful in analysis and simulation. The latter, on the other hand was widely accepted and used in simulations. However, recently the researcher has started to focus on the alternative mobility models with different mobility characteristics. This paper is focusing on the development of accurate models based on the Bayesian approach describing the random movement of nodes in the group of Random-based Mobility Model in mobile wireless networks. The Bayesian approach to adaptive filtering exploits the a priori information in a stationary parameter variation model to optimize adaptive filtering performance. The prior information contains two critical parameter characteristics: the variance (magnitude) of the various filter coefficients and their variation spectrum (power delay profile and Doppler spectrum in the case of wireless channel tracking). The practical tool for implementing Bayesian Adaptive Filtering (BAF) is the Kalman filter, which typically models the parameter variation as an AR(1) process. To further limit the complexity to the same order as the complexity of the RLS algorithm, a diagonal AR(1) model can be taken. The hyperparameters in the resulting state model can be estimated with the EM approach. In this paper, we analyze the effect of power delay profile and Doppler bandwidth on the steady-state performance of BAF and LMS and RLS algorithms. The approximation effects of using a simplified state model are also exhibited. [ABSTRACT FROM PUBLISHER]

This paper describes the basic information of QR Code. Especially for the blurred QR Code image, we put forward an improved NAS-RIF algorithm which makes bimodal characteristic of the barcode image into it as a constraint. NAS-RIF algorithm itself has a good convergence speed. It will effectively improve the recovery effect of barcode image after combining with bimodal limit. The experiment shows that the improved algorithm not only enhances PSNR but improves the visual effect of the restored barcode image. [ABSTRACT FROM PUBLISHER]

The problem discussed in this paper is that of estimating the period of a sequence of periodic events when the measurements of the occurrence times are noisy and sparse. The problem is common to many signal processing applications, such as baud estimation from zero-crossings in telecommunications and in pulse repetition interval estimation in electronic support measures. Previous algorithms have been based on periodogram maximisation [1, 2], Euclidean algorithms [3–5], least-squares line search [6], lattice line search [7] and Gaussian maximum likelihood [8]. Until now, very little has been known about the asymptotic statistical properties of any such algorithm. In this paper, a new algorithm is proposed, based on a modified least-squares approach. Under very general properties, the estimators of the system parameters are shown to have excellent (theoretical) asymptotic statistical properties. These properties are illustrated using a number of simulations. [ABSTRACT FROM PUBLISHER]

A novel algorithm is presented in this paper to design sparse FIR filters in the weighted least-squares (WLS) sense. The original design problem is cast as a constrained l0-norm optimization problem. To tackle the nonconvexity, an efficient iterative procedure is developed. In each iterative step, a subproblem in a simpler form is constructed. It can be demonstrated that in each iteration an optimal solution to each subproblem can be efficiently and reliably attained by the successive activation algorithm proposed in this paper, such that the overall design algorithm can converge to a local solution of the original design problem. Since its major part only involves scalar operations, compared with other sparse filter design approaches, the proposed design algorithm is computationally efficient. The effectiveness of the proposed design algorithm is demonstrated by numerical examples. [ABSTRACT FROM PUBLISHER]

Analog circuits that use on-chip digital-to-analog converters for calibration use a DSP based algorithm for adapting and calibrating the system parameters. In this paper, we show that this conventional method suffers from artifacts due to quantization noise which adversely affects the real-time and precise convergence to the desired parameters. We propose a ΣΔ based gradient-descent learning that can noise-shape the quantization noise during the adaptation procedure and in the process achieve faster convergence compared to the conventional quantized gradient-descent approach. We also show that when the analog circuits suffer from non-linearities and non-monotonic response of the calibration DACs, the proposed algorithm is still able to find the optimal system solution without getting trapped into local minima. Using measured results obtained from prototype fabricated in a 0.5-µm CMOS process, we demonstrate the robustness of the proposed algorithm for the task of: (a) compensating and tracking of offset parameters; and (b) calibration of the center frequency of a sub-threshold gm-C biquad filter. [ABSTRACT FROM PUBLISHER]

In this paper, we consider the theoretical bound of the probability of error in compressed sensing (CS) with the Bayesian approach. In the detection problem, the signal is sparse and is reconstructed from a compressed measurement vector. Utilizing the oracle estimator in CS, we provide a theoretical bound of the probability of error when the noise in CS is white Gaussian noise (WGN). We show that without any additional information in CS, the probability of error obtained using the signal reconstructed by four recovery algorithms: the basis pursuit denoising (BPDN) algorithm, the Dantzig selector, the orthogonal matching pursuit (OMP) method and the compressive sampling matching pursuit (CoSaMP) algorithm is always larger than the derived theoretical bound. Simulation results demonstrate the effectiveness of our result. [ABSTRACT FROM PUBLISHER]

In this paper, we propose adaptive texts detection and localization algorithms based on an edge extraction approach. The main techniques include edge filtering, adaptive threshold, the morphological operator and pixel correlation. With block-based processing, the rough texts can be detected by edge strength. The morphological operation is employed to refine the text region and to remove discrete non-text marks as well. The final text region can be accurately localized with a rectangle by horizontal differential approach. Simulations show that the proposed method can attain higher accuracy and lower false detection compared with the recent top paper [ABSTRACT FROM PUBLISHER]

This paper presents Weight Computing in Competitive K-Means Algorithm which is derived from Improved K-means method and subspace clustering. By adding weights to the objective function, the contributions from each feature of each clustering could simultaneously minimize the separations within clusters and maximize the separation between clusters. The experiments described in this paper confirm good performance of the proposed algorithm. [ABSTRACT FROM PUBLISHER]

Design and evaluation of a CORDIC (COordinate Rotation DIgital Computer) algorithm for a floating-point division operation is presented in this paper. In general, division operation based on CORDIC algorithm has a limitation in term of the range of inputs that can be processed by the CORDIC machine to give proper convergence and precise division operation result. A hardware architecture of CORDIC algorithm capable of processing broader input ranges is implemented and presented in this paper by using a pre-processing and a post-processing stage. The performance as well as the calculation error statistics over exhaustive sets of input tests are evaluated. The results show that the CORDIC algorithm can be well-convergence and gives precise division operation results with broader input ranges. The proposed hardware architecture is modeled in VHDL and synthesized on a CMOS standard-cell technology and a FPGA device, resulting 1 GFlops on the CMOS and 210.812 MFlops on the FPGA device. [ABSTRACT FROM PUBLISHER]

This paper considers development and evaluation of 2D frequency domain image formation algorithms for a FMCW array radar which forms a SAR-like 2D image of the surface in front of it. Implementations of the algorithms using interpolation as well as using the scaled Fourier transform with no interpolation are presented in the paper. Effectiveness of the algorithms are estimated and real data based images are presented. [ABSTRACT FROM PUBLISHER]

Automatic classification of modulation type in detected signals is an intermediate step between signal detection and demodulation, and is also an essential task for an intelligent receiver in various civil and military applications. In this paper, a new blind classification method is proposed for additive white Gaussian noise (AWGN) channels with unknown or variable signal to noise ratios. The algorithm is capable to adapt to the input SNR. In this algorithm, a passive-aggressive learning algorithm is applied to high confidence classified samples in a general classifier that is trained by different SNR signals. The selection of appropriate features helps the general system to work for a set of initial samples of each class. Simulation results show that the accuracy of the proposed algorithm approaches to a well-trained system in the target SNR, even in low SNRs. [ABSTRACT FROM PUBLISHER]

There are a large variety of applications that require considering sources that usually behave light or strong dependence and this is not the case that common blind signal separation (BSS) algorithms can do. The purpose of this paper is to develop non-parametric BSS algorithm for linear dependent source signals, which is proposed under the framework of contrast method. The contrast function is derived from the Schweizer-Wolff measure of pairwise dependence between the variables. Simulation results show that the proposed algorithm is able to separate the dependent signals and yield ideal performance. [ABSTRACT FROM PUBLISHER]

As one of biometric identification technologies, speaker recognition shows better application prospects in many fields. At present, the implementation of speaker recognition algorithm on the hardware is mostly based on System on a Programmable Chip(SOPC) platform of Field Programmable Gate Array(FPGA) with Nios II Intellectual Property(IP) core. And the algorithm can be selected and optimized effectively on SOPC. However the high-speed and parallel operation of FPGA can't be fully utilized. By researching and analyzing the speaker recognition algorithm, a FPGA-based speaker recognition method is presented in this paper. This method includes the Voice Active Detection (VAD), Mel Frequency Cepstrum Coefficient (MFCC) extraction and Vector Quantization (VQ) recognition algorithm. By using the technology such as Ping-pang operation, Pipeline and module reuse, it makes full use of FPGA's speed. After testing, this method can effectively meet the requirement of real-time data processing. [ABSTRACT FROM PUBLISHER]

The residue number system offers parallel processing, digital hardware, implementations for the binary operations of addition, subtraction and multiplication. This paper discusses the use of the residue number system in implementing digital signal processing functions, in which these binary operations abound. The paper covers implementations using arrays of read only memories, and briefly discusses the use of parallel microprocessor structures. ROM array implementations of scaling operations are also presented. [ABSTRACT FROM PUBLISHER]

BP neural network and DS evidence theory have gotten a wide range of applications in the field of information fusion. According to the BP neural network have low recognition rate and poor network stability, what is more, it is difficult to get D-S evidence theory of basic probability distribution function, This paper design a kind of improved algorithm, which combined group neural network and D-S evidence theory. The improved algorithm make full use of the advantages. The simulation results show that this algorithm have a better effect both in recognition rate and anti-noise capacity. [ABSTRACT FROM PUBLISHER]

Filtering being one of the most important modules in signal processing paradigm, this paper presents an FPGA implementation of various window-functions using CORDIC algorithm to minimize area-delay product. The existing window-architecture uses a linear CORDIC processor in series with circular CORDIC processor, that results in a long pipeline. Firstly, we replace the linear CORDIC with multiple optimized shift-add networks to reduce area and pipeline depth. Secondly, the conventional circular CORDIC processor is replaced by a completely scaling-free CORDIC processor to further improve the area-time efficiency of the existing design. As a result, the proposed window-architecture, on an average requires approximately 64.34% less pipeline stages and saves upto 48% area. Both the existing and the proposed window-architecture are capable of generating Hanning, Hamming and Blackman window families. [ABSTRACT FROM PUBLISHER]

3-D recursive search block matching is a conventional motion estimation method based on block matching. The algorithm is more efficient and effective than a full-search algorithm. However, the algorithm tends to fail to detect the true motion when the spatiotemporal smoothness of motion is not held. In this paper, we propose a new motion estimation algorithm introducing hierarchical search strategy. [ABSTRACT FROM PUBLISHER]

Super-resolution using web images is a challenging problem because accurate motion estimation is difficult due to their viewpoint change and photometric variation. To overcome such difficulties, this paper formulates a unified framework where local color correction is incorporated into fuzzy motion estimation after global registration. Thus, both geometric and photometric variations are simultaneously taken into account for implicit motion estimation. As demonstrated in the results using web images under various conditions, the proposed approach magnifies user-selected region of interest with visually appealing details. [ABSTRACT FROM PUBLISHER]

This paper proposes a novel algorithm for parking motion of a Car-like mobile robot. The algorithm presented here addresses calculating equations for planning a parking path in real time. Moreover, by incorporating the constraints of the mechanical and kinematical characteristics of the car and the geometry of the parking lot in the path planning, we can turn a parking problem into solving algebraic equations. By tracking a planned path, the Car-like mobile robot can drive into the parking area without hitting any boundaries. The efficiency of the proposed algorithm is demonstrated by simulation. [ABSTRACT FROM PUBLISHER]

The phase retrieval problem consisting of estimating the phase of a transfer function from measurement of its magnitude has been around for several years in the EMC community. In the first part of the paper instead of the direct application of the Hilbert integral, two alternative approaches are briefly illustrated: the cepstrum algorithm and the iterative method. They are used in the signal processing algorithm combining the estimation and detection procedures in sequence to identify the structure of an unknown transfer function: in the first step we determine the poles and zeros of the minimum phase transfer function (estimation), in the second step we find out the actual transfer function by comparing its output related to the unknown zero pole pattern with the outputs related to all the possible zero and pole patterns (detection). The structure of the model is particularly suitable to implement the parametric model, such as the Steiglitz Mc Bride algorithm. [ABSTRACT FROM PUBLISHER]

The structure of block sparsity in multi-band signals is prevalent. Among the block-sparse signal problems for compressive sensing, the most presenting recovery algorithms require block sparsity as prior information, whereas it may not be available in many practical applications. In this paper, a block sparsity adaptive regularized orthogonal matching pursuit algorithm (BAROMP) for compressive sensing is presented. The proposed algorithm could guarantee the accuracy of recovery by both the adaptive process which chooses the candidate set automatically and the regularization process which decides the atoms in the final support set. The simulation results show that the recovery probability of BAROMP which does not require sparsity as prior information is near to BROMP. [ABSTRACT FROM PUBLISHER]

This paper investigates the consensus problem of multi-agent networks with aperiodic sampled communication and nonuniform time-delays under switching topology. A distributed impulsive algorithm is introduced to achieve consensus, where only position measurements are utilized. It is shown that under certain assumptions upon the sampling intervals, the diagonal elements of Laplace matrix and the control gains, consensus of the controlled multi-agent network is achieved if the communication graph has a spanning tree jointly. A numerical example is given to illustrate the effectiveness of the proposed algorithm. [ABSTRACT FROM PUBLISHER]

In cognitive radio (CR) systems, efficient spectrum sensing ensures secondary users (SUs) to successfully access the spectrum hole. Typically, the detection problem has been studied separately from the optimization of throughput of secondary network. However, due to non-zero probabilities of miss detection and false alarm, the sensing phase has an impact on the throughput of CR networks as well as on the transmission of primary users (PUs). In this paper, using energy detection, we maximize the total throughput of all SUs by jointly optimizing the detection threshold and resource allocation in multiband CR systems. Efficient algorithms including online and offline solutions are proposed to solve the proposed mix-integer programming problem, which show better performance compared with traditional uniform detection threshold selection algorithm. [ABSTRACT FROM PUBLISHER]

Automatic recognition of communication standards is a crucial task, if universal interoperability between different communication systems is claimed. A gateway performing the recognition task can be used to interconnect heterogeneous systems with different and incompatible protocol waveforms. This paper proposes an algorithm for the automatic recognition of various communication standards used for wireless smart home networks in the European 868 MHz Short-Range Device frequency band. Relevant statistical key features are extracted from the received signal and the recognition is based on threshold comparison. The algorithm is appropriate for the implementation in low-complexity embedded systems and works over a wide range of signal-to-noise ratios (SNR). [ABSTRACT FROM PUBLISHER]

This paper considers consensus control problem of multiple nonlinear systems with uncertainty. Consensus algorithms are proposed with the aid of Lyapunov techniques and results from graph theory. To show the effectiveness of the proposed algorithms, simulation results are presented. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a cognitive approach to design phase-only modulated waveforms sharing a desired ambiguity function. We formulate the problem as a quartic order polynomial optimization problem with a constant modulus constraint on each variable. Thus, we devise an optimization procedure based on the Maximum Block Improvement (MBI) approach. At the analysis stage, we assess the performance of the proposed technique showing its capability to properly shape the ambiguity function of the transmitted waveform. [ABSTRACT FROM PUBLISHER]

This paper presents two fast and computationally efficient interferometric algorithms that allow high accuracy, unambiguous angle-of-arrival (AOA) estimates to be obtained using physically large wideband antennas that are suitable for electronic surveillance. The performance of the algorithms are compared against conventional interferometric techniques using simulations and are also verified experimentally. [ABSTRACT FROM PUBLISHER]

In this paper we propose an efficient transmission strategy for the two-way relay channel (TWRC) with multiple relays, when these are multiple-input multiple-output (MIMO) transceivers that apply the amplify-and-forward (AF) protocol. Although the optimal beamforming strategy is known, it requires a central node, with channel state information (CSI) of the entire network, to compute all the beamforming matrices, which is impractical. To reduce the overhead, in this paper we present a distributed algorithm for the computation of the relay beamforming matrices. The proposed algorithm divides the problem in two stages. First, each relay computes its own beamforming matrix in parallel using only local CSI. Next, a distributed beamforming is applied to make the signals add up coherently at the nodes. Although the proposed algorithm is suboptimal, we show through simulations that it performs very close to the optimal achievable rate region. [ABSTRACT FROM PUBLISHER]

This paper investigates an adaptive beamforming technique that is robust against imperfect knowledge of signal and interference arrival directions, antenna calibration errors and other similar imperfections. The proposed technique is based on Generalized Sidelobe Canceller but uses Slepian sequences in both the steering and blocking lobes of the algorithm. Only Slepians corresponding to large spectral concentrations are used in the branches. The theoretical results are verified in a simulation study. [ABSTRACT FROM PUBLISHER]

This paper investigates the theoretical requirements for unique localization of multiple emitters using time delay of arrival (TDoA) subjected to the data-association problem. Specifically, an examination is carried out to find the necessary fundamental requirements to solve the so-called ghost node problem pertaining to sensor arrays. [ABSTRACT FROM PUBLISHER]