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This paper introduces an adaptive filtering process based on shrinking wavelet coefficients from the corresponding signal wavelet representation. The filtering procedure considers a threshold method determined by an iterative algorithm inspired by the control charts application, which is a tool of the statistical process control (SPC). The proposed method, called SpcShrink, is able to discriminate wavelet coefficients that significantly represent the signal of interest. The SpcShrink is algorithmically presented and numerically evaluated according to Monte Carlo simulations. Two empirical applications to real biomedical data filtering are also included and discussed. The SpcShrink shows superior performance when compared with competing algorithms., 19 pages, 10 figures, 2 tables

The Linear Mixing Model (LMM) of hyperspectral images asumes that pixel spectra are affine combinations of basic spectral signatures, called endmembers, which are the vertices of a convex polytope covering the image data. Endmember induction algorithms (EIA) extract the endmembers from the image data, obtaining a precise spectral characterization of the image. The WM algorithm assumes that a set of Affine Independent vectors can be extracted from the rows and columns of dual Lattice Autoassociative Memories (LAAM) built on the image spectra. Indeed, the set of endmembers induced by this algorithm defines a convex polytope covering the hyperspectral image data. However, the number of induced endmembers obtained by this procedure is too high for practical purposes, besides they are highly correlated. In this paper, we apply a greedy sparsification algorithm aiming to select the minimal set of endmembers that explains the data in the image. We report results on a well known benchmark image.

Solving inverse problems, where we find the input values that result in desired values of outputs, can be challenging. The solution process is often computationally expensive and it can be difficult to interpret the solution in high-dimensional input spaces. In this paper, we use a problem from additive manufacturing to address these two issues with the intent of making it easier to solve inverse problems and exploit their results. First, focusing on Gaussian process surrogates that are used to solve inverse problems, we describe how a simple modification to the idea of tapering can substantially speed up the surrogate without losing accuracy in prediction. Second, we demonstrate that Kohonen self-organizing maps can be used to visualize and interpret the solution to the inverse problem in the high-dimensional input space. For our data set, as not all input dimensions are equally important, we show that using weighted distances results in a better organized map that makes the relationships among the inputs obvious., 16 figures and 4 tables

The problem of error detection and correction (both transient and permanent) in linear digital state variable systems, a very large class of circuits used in digital signal processing and control, is considered. The case of single faulty modules (adders, multipliers, shifters, etc.) is studied, and general circuit data flow graphs (with and without fanout) that realize linear digital state variable systems are analyzed to determine how additional system states might be added to the data flow graph to achieve error detection and correction. It is seen that error detection and correction can be achieved by the addition of a relatively small amount of additional hardware which functions as the checking circuitry. Next, error detection under multiple faulty modules with and without fanout of the module outputs is studied. An analysis tool called the gain matrix is introduced. The problem of fault location and correction of single faults is discussed. Recursive as well as nonrecursive systems can be handled. >

Recent advances in neurophysiology have led to the development of complex dynamical models that describe the connections and causal interactions between different regions of the brain. These models are able to accurately mimic the event-related potentials observed by EEG/MEG measurement systems, and are considered to be key components for understanding brain functionality. In this paper, we focus on a class of nonlinear dynamic causal models (DCM) that are described by a set of connectivity parameters. In practice, the DCM parameters are inferred using data obtained by an EEG or MEG sensor array in response to a certain event or stimulus, and then used to analyze the strength and direction of the causal interactions between different brain regions. The usefulness of these parameters in this process will depend on how accurately they can be estimated, which in turn will depend on noise, the sampling rate, number of data samples collected, the accuracy of the source localization and reconstruction steps, etc. The goals of this paper are to present several algorithms for DCM parameter estimation, derive Cramer-Rao performance bounds for the estimates, and compare the accuracy of the algorithms against the theoretical performance limits under a variety of circumstances. The influence of noise and sampling rate will be explicitly investigated.

With the increasingly integration of distributed generator (DG) in active distribution network (ADN), the fault location problem is facing more challenges. This paper proposes a hierarchical fault location model for ADN, in which the upper-layer model is to determine the area location of the fault and the lower-layer model is to get the section location of the fault. To solve the hierarchical fault location model in an efficient way, a hybrid solving strategy combing the genetic algorithm and the integer linear programming method is applied. Case studies are carried out on a modified IEEE 33-bus distribution network, in which the DGs are of high penetration. The results show that when a single or multiple fault occurs in the ADN, the hierarchical fault location method can detect the fault location more accurately than the traditional single-layer fault location method. And the results also indicate that the model and algorithm used in this paper perform significantly better than the single model and single algorithm in terms of convergence speed, accuracy, and fault tolerance.

This paper presents a general and systematic approach for geometric error modeling of machine tools due to the geometric errors arising from manufacturing and assembly. The approach can be implemented in three steps: (1) development of a linear map between the pose error twist and source errors within machine tool kinematic chains using homogeneous transformation matrix method; (2) formulation of a linear map between the pose error twist and the error intensities of a machine tool; (3) combination of these two models for error separation. The merit of this approach lies in that it enables the source errors affecting the compensatable and uncompensatable pose accuracy of the machine tool to be explicitly separated, thereby providing designers and/or field engineers with an informative guideline for the accuracy improvement by suitable measures, i.e. component tolerancing in design, manufacturing and assembly processes, and error compensation. Two typical multi-axis machine tools are taken as examples to illustrate the generality and effectiveness of this approach.

360-degree videos have become increasingly popular with the application of virtual reality (VR) technology. To encode such kind of videos with ultra-high resolution, an efficient and real-time video encoder becomes a key requirement. The Versatile Video Coding (VVC) standard has good coding performance. However, it has pretty high computational complexity which increasing the application cost of 360-degree videos. Among them, the decision of the quadtree with nested multi-type tree (QTMT) partitioning structure is one of the time-consuming procedures. In this paper, based on the characteristics of 360-degree video with Equirectangular projection (ERP) format, the empirical variogram combined with Mahalanobis distance is introduced to measure the difference between the horizontal and vertical directions of the CU, and a fast partition algorithm is proposed. The experimental results show that the algorithm saves 32.13% of the coding time with only an increase of 0.66% in BDBR.

Autoregressive state transitions, where predictions are conditioned on past predictions, are the predominant choice for both deterministic and stochastic sequential models. However, autoregressive feedback exposes the evolution of the hidden state trajectory to potential biases from well-known train-test discrepancies. In this paper, we combine a latent state space model with a CRF observation model. We argue that such autoregressive observation models form an interesting middle ground that expresses local correlations on the word level but keeps the state evolution non-autoregressive. On unconditional sentence generation we show performance improvements compared to RNN and GAN baselines while avoiding some prototypical failure modes of autoregressive models., Comment: emnlp camera ready

Renewable energy power prediction is crucial to economic dispatch and reliable operation of power systems. This paper proposes a wind power forecasting approach based on the Extreme Gradient Boosting (XGBoost) algorithm. XGBoost is not only an effective feature selection method but also an accurate forecasting approach. In order to avoid excessive manual interventions for hyperparameter tuning, the Tree-Structured Parzen Estimator (TPE) model is presented to optimize the hyperparameters of XGBoost. This forecasting strategy has been tested in a real wind farm in Spain, compared with Persistence and Support Vector Regression (SVR). The results show that the XGBoost algorithm has higher accuracy and is a novel effective approach for very short-term wind power prediction.

With the present paper, the author proposes a fitting method for approximating experimental data retrieved from any full-field technique. Unlike most of the fitting procedures, the method works on data distributed on a surface of any shape, and the mathematical model is able to take into account of both the 3D shape of the surface and of the experimental quantity to be fitted. The paper reports all the mathematical steps necessary for applying the method, which was tested on two sets of experimental data obtained by an out-of-plane speckle interferometer working in two different conditions of noise. Experimental results showed the capability of the method to work in presence of high level of noise.

Alexander Gebharter (2017) has proposed to use one of the best known Bayesian network (BN) causal discovery algorithms, PC, to identify the constitutive dependencies underwriting mechanistic explanations. His proposal assumes that mechanistic constitution behaves like deterministic direct causation, such that PC is directly applicable to mixed variable sets featuring both causal and constitutive dependencies. Gebharter claims that such mixed sets, under certain restrictions, comply with PC’s background assumptions. The aim of this paper is to show that Gebharter’s proposal incurs severe problems, ultimately rooted in the widespread non-compliance of mechanistic systems with PC’s assumptions. This casts severe doubts on the attempt to implicitly define constitution as a form of deterministic direct causation complying with PC’s assumptions.

The main contributions of this paper are twofold. On the one hand, the twin Diffie-Hellman (twin DH) problem proposed by Cash, Kiltz and Shoup is extended to the n-Diffie-Hellman (n-DH) problem for an arbitrary integer n, and this new problem is shown to be at least as hard as the ordinary DH problem. Like the twin DH problem, the n-DH problem remains hard even in the presence of a decision oracle that recognizes solution to the problem. On the other hand, observe that the double-size key in the Cash et al. twin DH based encryption scheme can be replaced by two separated keys each for one entity, that results in a 2-party encryption scheme which holds the same security feature as the original scheme but removes the key redundancy. This idea is further extended to an n-party case, which is also known as n-out-of-n encryption. As examples, a variant of ElGamal encryption and a variant of Boneh-Franklin IBE have been presented; both of them have proved to be CCA secure under the computational DH assumption and the computational bilinear Diffie-Hellman (BDH) assumption respectively, in the random oracle model. The two schemes are efficient, due partially to the size of their ciphertext, which is independent to the value n.

The paper introduces a numerical tool based on a predictor–corrector continuation algorithm to obtain the bifurcation analysis of a perfectly stirred reactor with detailed reaction mechanisms. Each step of the continuation algorithm is reviewed and adapted to handle reaction mechanisms with hundreds of species and thousands of reactions. Particularly, the adoption of a Broyden solver in the predictor–corrector algorithm and a new formulation of the test functions are proposed. The implementation in Matlab and the adoption of the CANTERA Toolbox, make the tool easily applicable to reaction mechanisms available in CHEMKIN format. To validate and demonstrate the capability of the tool, the full equilibrium curves have been obtained for three different cases, having increasing number of species and reactions: methane–air (GRIMech.1.2), simple surrogates of Jet-A in air (JetSurF2.0) and a ternary surrogate of Jet-A in air (CRECK). The tool gets performances that make affordable the computations even with desktop computers.

Direct-sequence code-division multiple-access (DS-CDMA) is a popular multiple-access technology for wireless communications. However, its performance is limited by multiple-access interference and multipath distortion. Multiuser detection and space-time processing are two signal processing techniques employed to improve the performance of DS-CDMA. Two minimum probability of error-based space-time multiuser detection algorithms are proposed in this paper. The first algorithm, minimum joint probability of error (MJPOE), aims to minimize the joint probability of error for all users. The second algorithm, minimum conditional probability of error (MCPOE), minimizes the probability of error of each user conditioned on the transmitted bit vector, for each user individually. In both the algorithms, the optimal filter weights are computed adaptively using a gradient descent approach. The MJPOE algorithm is blind and offers a bit-error-rate (BER) performance better than the nonadaptive minimum mean squared error (MMSE) algorithm, at the cost of higher computational complexity. An approach for reducing the computational overheads of MJPOE using Gram-Schmidt orthogonalization is suggested. The BER performance of the MCPOE algorithm is slightly inferior to MMSE, however, it has a computational complexity linear in the number of users. Both blind and training-based implementations for MCPOE are proposed. Both MJPOE and MCPOE have a convergence rate much faster than earlier known adaptive implementations of the MMSE detector, viz. least mean square and recursive least squares. Simulation results are presented for synchronous single path channels as well as asynchronous multipath channels, with multiple antennas employed at the receiver., IEEE

This paper investigates the elliptic localization for moving object problem from time delay (TD) and Doppler frequency shift (DFS) measurements, where the transmitter position and velocity are unknown. The transmitter is not perfectly time syncronized such that unknown offsets exist in the TD and DFS measurements. We propose to jointly estimate the object and transmitter positions and velocities and the offsets. Using the TD and DFS measurements from both the indirect and direct paths between the transmitter and the receivers, we formulate a non-convex weighted least squares (WLS) problem. Local convergence may occur when solving the non-convex WLS problem, implying that good estimate is not guaranteed. Thus, we relax the non-convex WLS problem into a convex semidefinite program by applying semidefinite relaxation (SDR). Moreover, we theoretically show that the performance can be improved by using multiple transmitters as compared to that using single transmitter, although more unknown parameters are introduced. We then extend the proposed SDR method to handle the multiple transmitters case. Finally, the mean square error analysis is provided to show that the proposed WLS method reaches the Cramer-Rao lower bound accuracy under small Gaussian noise condition. Simulation results validate the theoretical analysis and show the superior performance over the existing methods.

Degree-of-freedom (DoF)-based models have been proven to be highly successful in modeling and analysis of MIMO systems. Among existing DoF-based models, the number of DoFs used for interference cancellation (IC) is solely based on the number of interfering data streams. However, from both experimental and simulation results, we find that signal strengths of an interference link vary significantly in different directions in the eigenspace. In this paper, we exploit the difference in interference signal strengths in the eigenspace and perform IC with DoFs only on those directions with strong signals. To differentiate interference signal strengths on an interference link, we introduce a novel concept called ‘`effective rank threshold.’' Based on this threshold, DoFs are consumed only to cancel strong interferences in the eigenspace while weak interferences are treated as noise in throughput calculation. To better understand the benefits of this approach, we study a fundamental trade-off between network throughput and effective rank threshold for an MU-MIMO network. Our simulation results show that network throughput under optimal rank threshold is significantly higher than that under existing DoF IC models. To ensure the new DoF IC model is feasible at PHY layer, we propose an algorithm to set the weights for all nodes that can offer our desired DoF allocation.

Some video applications require the use of extremely simple source coding techniques. For these applications, Modulo-PCM is an interesting alternative to PCM since it has a very low complexity and performs better than PCM. In this paper, we present an adaptive Modulo-PCM algorithm for image coding. Our algorithm divides the image into blocks and performs a proper Modulo-PCM coding of each block. Since our algorithm allows different degrees of encoding complexity, it can adapt to the computational resources that are available in each video application. Experimental results show that our algorithm improves the coding efficiency of both non-adaptive MPCM and PCM. The magnitude of the improvements depends on the encoding complexity and the target rate.

Traditional approximate point-in-triangulation test (APIT) localization algorithm requiring low equipped hardware, having relatively high location accuracy, is easy to implement, and widely used in wireless sensor network positioning system. However, the location accuracy of unknown node in triangle overlap region should be further improved, especially in the sparse beacons' environment, the location accuracy is seriously affected. In this paper, MC-APIT algorithm is proposed, which implements random sampling using the Monte Carlo method in the overlap region, and filters samples through the target node's RSSI (Received Signal Strength) sequence values, in order that Mathematical expectation of the sample values could converge to that of the target node'. Simulation results show that: the algorithm can reduce the sampling area and the location energy consumption, to a certain extent restrained the propagation error. Compared with APIT algorithm, the location accuracy has been markedly improved.

Both the growing number of dispersed generation plants and storage systems and the new roles and functions on the demand side (e.g. demand side management) are making the operation (monitoring and control) of electrical grids more complex, especially in distribution. This paper demonstrates how to integrate phasor measurements so that state estimation in a distribution grid profits optimally from the high accuracy of PMUs. Different measurement configurations consisting of conventional and synchronous measurement units, each with different fault tolerances for the quality of the calculated system state achieved, are analyzed and compared. Weighted least squares (WLS) algorithms for conventional, linear and hybrid state estimation provide the mathematical method used in this paper. A case study of an 18-bus test grid with real measured PMU data from a 110 kV distribution grid demonstrates the improving of the system's state variable's quality by using synchrophasors. The increased requirements, which are the prerequisite for the use of PMUs in the distribution grid, are identified by extensively analyzing the inaccuracy of measurement and subsequently employed to weight the measured quantities.

This paper proposed a tensor completion algorithm by tracking the Parallel Factor (PARAFAC) decomposition of incomplete third-order tensors with one dimension growing with time. The proposed algorithm first tracks a low-dimensional subspace, then updates the loading matrices of the PARAFAC decomposition. Simulation results showed that the algorithm is reliable and fast, in comparison to the state-of-the-art PARAFAC Weighted OPTimization algorithm.

One of the most challenging problems that we may face in natural language processing tasks is the lack of annotated training datasets. In this paper our goal is to consider the impact of Active Learning algorithm on a cross-lingual model in sentiment analysis task on Persian language which is known as a low-resource language. Cross-lingual model trains a model by using a rich-resource language like English as a source language and apply it to a low-resource language, in this way the dependency to training datasets is decreased. Also using Active Learning strategy helps us to improve the functionality of our model by selecting most representative samples. Since labeling data is expensive and time consuming, by selecting the machine desirable data we can reduce the amount of labeled data required for our tasks. To do this we can select data which classifier is the least confident about them. When they are chosen, a user is asked to labeled them. There are lots of methods and factors to choose the appropriate data for Active Learning strategy. In the end these methods help our classifier to gain more knowledge about samples and work more properly.

Being one non-orthogonal multiple access (NOMA) scheme, interleave-division multiple access (IDMA) can increase the capacity of wireless communication systems, and therefore has drawn many attentions. In this paper, polar codes, which have been adopted by 3GPP eMBB control channel, are applied to OFDM-IDMA systems for further performance improvement. The corresponding joint detection and decoding (JDD) scheme is proposed as well. To further reduce the decoding complexity, a sign aided JDD (SA-JDD) scheme is introduced. According to numerical results, the proposed polar-coded OFDM-IDMA scheme outperforms the uncoded one. The proposed JDD scheme has nearly 1.5 dB gain over the separated detection and decoding (SDD) scheme when BER = 4 × 10−3. SA-JDD scheme requires only half complexity of JDD with no performance degradation when SNR = 8 dB. Finally, the corresponding hardware architecture is also proposed.

This paper proposes a test to check the specification of models with unobserved individual effects integrated out by quadrature and also a simple way of increasing the flexibility of this type of model. The results of a Monte Carlo study and an application using a well-known data set illustrate the finite sample properties of the proposed methods and their implementation in practice.

An algorithm using fixed-lag smooth filtering to improve the precision of EIS algorithm is presented in this paper. Firstly, gray projection algorithm (GPA) is used to calculate the interframe global motion vectors. Secondly, the fixed-lag smooth filter is used to smoothen the motion track of the original video sequence, according to different motion models. At last, the original sequence is compensated by using the compensation vectors calculated from filtering. This algorithm has been proved superior in filtering precision by experiments of video sequences in different motion scenes. This algorithm also satisfies the real-time request.

This paper considers identification of sparse Volterra systems. Two identification meth- ods based on the almost orthogonal matching pur- suit (AOMP) and the RIVAL (removing irrele- vant variables amidst Lasso iterations) algorithm are proposed. The AOMP algorithm allows one to estimate one non-zero coefficient at a time until all non-zero coefficients are found without losing the optimality and the sparsity, thus avoiding the curse of dimensionality often encountered in Volterra sys- tem identification. However, the conditions for the AOMP are strong. To this end, the RIVAL is pro- posed that solves the set identification and param- eter estimation problems simultaneously with the convergence results, and outperforms the standard Lasso-type algorithms.