Batch Version Research Articles

Dynamics identification and control of nonlinear MIMO coupled plant using supervised neural gas and comparison with recurrent neural controller

The dynamics identification and subsequent control of a nonlinear system is not a trivial issue. The application of a neural gas network that is trained with a supervised batch version of the algorithm can produce identification models in a robust way. In this paper, the neural model identifies each local transfer function, demonstrating that the local linear approximation can be done. Moreover, other parameters are analyzed in order to obtain a correct modeling. Furthermore, the algorithm is applied to control a nonlinear multi-input multi-output system composed of tanks. In addition, this plant is a coupled system where the manipulated input variables are influencing all the output variables. The aim of the work is to demonstrate that the supervised neural gas algorithm is able to obtain linear models to be used in a state space design scenario to control nonlinear coupled systems and guarantee a robust control method. The results are compared with the common approach of using a recurrent neural controller trained with a dynamic backpropagation algorithm. Regarding the steady-state errors in disturbance rejection, reference tracking and sensitivity to simple process changes, the proposed approach shows an interesting application to control nonlinear plants.

Efficient batch identity‐based fully homomorphic encryption scheme in the standard model

Identity-based fully homomorphic encryption (IBFHE) provided a fundamental solution to the problem of huge public key size that exposed in fully homomorphic encryption (FHE) schemes, thus it is significant to make FHE become more practical. In recent years, the construction of IBFHE schemes were mainly based on lattices due to their conjectured resistance against quantum cryptanalysis, however, which makes these cryptosystems further unpractical. The main reason is the trapdoor function on which the scheme was based is rather complex for practical and the ciphertext size is too large. In this study, the authors propose an efficient batch IBFHE scheme, which can be proven secure from the standard LWE assumption in the standard model. The first contribution of this work is that the authors construct an efficient batch version of MP12 preimage sampling algorithm, which can efficiently generate identity keys for multi-bit IBE schemes. Based on that, the authors construct an asymptotically-faster multi-bit IBE scheme as the second contribution. The third contribution is that the authors transform the multi-bit IBE scheme to batch IBFHE scheme which supports to encrypt any message in Z q n . Compared with the similar schemes, the authors show their schemes are essentially improved.

Improved mismatched filtering for ATV‐based passive bistatic radar

This study examines the problem of solving the Doppler ambiguity in analogue television (ATV)-based passive bistatic radar. When mismatched filtering is used to deal with the range ambiguity at 19.2 km, the Doppler ambiguity caused by the 50 Hz field frequency is equally rising because of the signal-to-noise ratio (SNR) loss in the main lobe. Aiming at this problem, an improved mismatched filtering algorithm is proposed. In this new algorithm, the Doppler ambiguity sidelobe suppressing is considered into the cost function, and the mismatched filtering factor is acquired based on minimising the SNR loss, the range sidelobes energy and the Doppler sidelobes energy. Moreover, a batch version is applied to lower the computation. Then the performance of the improved mismatched filtering algorithm is verified by the theoretical analysis. Finally, the application of real data proved the effectiveness of the improved algorithm.

MATLAB Toolboxes for Reference Electrode Standardization Technique (REST) of Scalp EEG

Reference electrode standardization technique (REST) has been increasingly acknowledged and applied as a re-reference technique to transform an actual multi-channels recordings to approximately zero reference ones in electroencephalography/event-related potentials (EEG/ERPs) community around the world in recent years. However, a more easy-to-use toolbox for re-referencing scalp EEG data to zero reference is still lacking. Here, we have therefore developed two open-source MATLAB toolboxes for REST of scalp EEG. One version of REST is closely integrated into EEGLAB, which is a popular MATLAB toolbox for processing the EEG data; and another is a batch version to make it more convenient and efficient for experienced users. Both of them are designed to provide an easy-to-use for novice researchers and flexibility for experienced researchers. All versions of the REST toolboxes can be freely downloaded at http://www.neuro.uestc.edu.cn/rest/Down.html, and the detailed information including publications, comments and documents on REST can also be found from this website. An example of usage is given with comparative results of REST and average reference. We hope these user-friendly REST toolboxes could make the relatively novel technique of REST easier to study, especially for applications in various EEG studies.

Carbocation Organocatalysis in Interrupted Povarov Reactions to cis‐Fused Pyrano‐ and Furanobenzodihydropyrans

Tritylium cation‐catalyzed interrupted Povarov reactions afforded cis‐4‐aminobenzodihydropyrans in excellent yields (90 %) within 10 min by low catalyst loadings (1 mol‐%). A mechanism involving Lewis acidic catalysis by a carbocation was proposed and validated. Changing a one‐pot batch version of the reaction into a two‐stage convergent continuous flow procedure led to a 10‐fold time reduction (to 1 min) with 88 % yield.

Automated energy simulation and analysis for NetZero Energy Home (NZEH) design

NetZero Energy Homes (NZEHs) have emerged as a solution to alleviate the energy demand from residential building operation, where appropriate design of building envelope and mechanical system is a means to achieving energy conservation and recovery for NZEHs. This research thus proposes an informed decision making framework for NZEH building design based on an automated energy simulation approach. The Batch Version of HOT2000 is utilized to achieve automated single-factor and combined-factor simulations, and a total of 16 200 combinations of building envelope and mechanical device design options are simulated for NZEH design. An NZEH project in Edmonton, Canada, is utilized as the case study in this research. The initial design of the NZEH results in an estimated energy deficit of 6048.0 MJ, accounting for 8.8% of the total consumption, and, based on the combined-factor simulations results, improved design scenarios are recommended for this NZEH. The simulation results of the initial design are also validated using the monitored data, with the actual performance showing an energy deficit of 4.1% of total consumption. Furthermore, such analysis as regression, factor importance ranking, and temperature set-point simulation are also conducted for the NZEH building design. This research proposes a framework to support informed design decision making for NZEHs, and builds a baseline for future study.

Parallel incremental whole-program optimizations for Scala.js

Whole-program optimizations are powerful tools that can dramatically improve performance, size and other aspects of programs. Because they depend on global knowledge, they must typically be reapplied to the whole program when small changes are made, which makes them too slow for the development cycle. This is an issue for some environments that require, or benefit a lot from, whole-program optimizations, such as compilation to JavaScript or to the Dalvik VM, because their development cycle is slowed down either by the lack of optimizations, or by the time spent on applying them. We present a new approach to designing incremental whole-program optimizers for object-oriented and functional languages: when part of a program changes, only the portions affected by the changes are reoptimized. An incremental optimizer using this approach for Scala.js, the Scala to JavaScript compiler, demonstrates speedups from 10x to 100x compared to its batch version. As a result, the optimizer's running time becomes insignificant compared to separate compilation, making it fit for use on every compilation run during the development cycle. We also show how to parallelize the incremental algorithm to take advantage of multicore hardware.

Optimal learning with Bernstein online aggregation

We introduce a new recursive aggregation procedure called Bernstein Online Aggregation (BOA). Its exponential weights include a second order refinement. The procedure is optimal for the model selection aggregation problem in the bounded iid setting for the square loss: the excess of risk of its batch version achieves the fast rate of convergence $$\log (M)/n$$log(M)/n in deviation. The BOA procedure is the first online algorithm that satisfies this optimal fast rate. The second order refinement is required to achieve the optimality in deviation as the classical exponential weights cannot be optimal, see Audibert (Advances in neural information processing systems. MIT Press, Cambridge, MA, 2007). This refinement is settled thanks to a new stochastic conversion that estimates the cumulative predictive risk in any stochastic environment with observable second order terms. The observable second order term is shown to be sufficiently small to assert the fast rate in the iid setting when the loss is Lipschitz and strongly convex. We also introduce a multiple learning rates version of BOA. This fully adaptive BOA procedure is also optimal, up to a $$\log \log (n)$$loglog(n) factor.

Batch Version of Extensive Cancellation Algorithm for Clutter Mitigation in Frequency Domain of Passive Radar

Passive radar experiences a significant problem called multipath clutter. The Batch version of the Extensive Cancellation Algorithm (ECA-B) is an efficient method for clutter mitigation. With the increase in signal bandwidth, a greater number of segments is required to cancel the clutter across the entire frequency range. This affects the processing rate, detrimentally weakening and modulating the signal from low-speed targets. Thus, this paper proposes a method that uses ECA-B to process both reference and echo signals in the frequency domain. This method not only reduces the amount of calculation required but also avoids weakening and modulating the target signal, which is spread across many segments. The simulated and experimental data results confirm the correctness and validity of the proposed method.

VariOtator, a Software Tool for Variation Annotation with the Variation Ontology.

The Variation Ontology (VariO) is used for describing and annotating types, effects, consequences, and mechanisms of variations. To facilitate easy and consistent annotations, the online application VariOtator was developed. For variation type annotations, VariOtator is fully automated, accepting variant descriptions in Human Genome Variation Society (HGVS) format, and generating VariO terms, either with or without full lineage, that is, all parent terms. When a coding DNA variant description with a reference sequence is provided, VariOtator checks the description first with Mutalyzer and then generates the predicted RNA and protein descriptions with their respective VariO annotations. For the other sublevels, function, structure, and property, annotations cannot be automated, and VariOtator generates annotation based on provided details. For VariO terms relating to structure and property, one can use attribute terms as modifiers and evidence code terms for annotating experimental evidence. There is an online batch version, and stand-alone batch versions to be used with a Leiden Open Variation Database (LOVD) download file. A SOAP Web service allows client programs to access VariOtator programmatically. Thus, systematic variation effect and type annotations can be efficiently generated to allow easy use and integration of variations and their consequences.

FUZZY EXTREME LEARNING MACHINE FOR A CLASS OF FUZZY INFERENCE SYSTEMS

Recently an Online Sequential Fuzzy Extreme Learning (OS-Fuzzy-ELM) algorithm has been developed by Rong et al. for the RBF-like fuzzy neural systems where a fuzzy inference system is equivalent to a RBF network under some conditions. In the paper the learning ability of the batch version of OS-Fuzzy-ELM, called as Fuzzy-ELM is further evaluated to train a class of fuzzy inference systems which can not be represented by the RBF networks. The equivalence between the output of the fuzzy system and that of a generalized Single-Hidden Layer Feedforward Network as presented in Huang et al. is shown first, which is then used to prove the validity of the Fuzzy-ELM algorithm. In Fuzzy-ELM, the parameters of the fuzzy membership functions are randomly assigned and then the corresponding consequent parameters are determined analytically. Besides an input variable selection method based on the correlation measure is proposed to select the relevant inputs as the inputs of the fuzzy system. This can avoid the exponential increase of number of fuzzy rules with the increase of dimension of input variables while maintaining the testing performance and reducing the computation burden. Performance comparison of Fuzzy-ELM with other existing algorithms is presented using some real-world regression benchmark problems. The results show that the proposed Fuzzy-ELM produces similar or better accuracies with a significantly lower training time.

Illumination-Robust Foreground Detection in a Video Surveillance System

This paper presents a foreground detection algorithm that is robust against illumination changes and noise, and provides a novel and practical choice for intelligent video surveillance systems using static cameras. This paper first introduces an online expectation-maximization algorithm that is developed from a basic batch version to update Gaussian mixture models in real time. Then, a spherical K-means clustering method is combined to provide a more accurate direction for the update when illumination is unstable. The combination is supported by the linearity of RGB color reflected from object surfaces, which is both theoretically proved by spectral reflection theory and experimentally validated in several observations. Foreground detection is carried out using a statistical framework with regional judgment. Noise in the detection stage is further reduced by a Bayesian iterative decision-making step. The experiments show that the proposed algorithm outcompetes several classical methods on several datasets, both in detection performance and in robustness to perturbations from illumination changes.

Adaptive Approximate Kernel Orthogonalization for Antenna Array Processing

In a previous paper we have presented a kernel method for antenna array processing using gaussian kernels as basis functions (Approximated Kernel Orthogonalization, AKO) and shown that it presents near optimal performance with an affordable computational burden and small size of the resulting model. However, AKO was only able to operate in batch mode, i.e., it had to process chunks of data (between 10 and 30 frames), which implies a delay in the system response and inability to rapidly respond to changes in the environment. We present here an adaptive extension, the so called Adaptive Approximate Kernel Orthogonalization (AAKO) Method, that is able to on-line process the received frames without loosing performance with respect to the batch version and without any delay in the output, which opens up the possibility of real time implementations.

Real-Time Motor Unit Identification From High-Density Surface EMG

This study addresses online decomposition of high-density surface electromyograms (EMG) in real time. The proposed method is based on the previously published Convolution Kernel Compensation (CKC) technique and shares the same decomposition paradigm, i.e., compensation of motor unit action potentials and direct identification of motor unit (MU) discharges. In contrast to previously published version of CKC, which operates in batch mode and requires ∼10 s of EMG signal, the real-time implementation begins with batch processing of ∼3 s of the EMG signal in the initialization stage and continues on with iterative updating of the estimators of MU discharges as blocks of new EMG samples become available. Its detailed comparison to previously validated batch version of CKC and asymptotically Bayesian optimal linear minimum mean square error (LMMSE) estimator demonstrates high agreement in identified MU discharges among all three techniques. In the case of synthetic surface EMG with 20 dB signal-to-noise ratio, MU discharges were identified with average sensitivity of 98%. In the case of experimental EMG, real-time CKC fully converged after initial 5 s of EMG recordings and real-time and batch CKC agreed on 90% of MU discharges, on average. The real-time CKC identified slightly fewer MUs than its batch version (experimental EMG, 4 MUs versus 5 MUs identified by batch CKC, on average), but required only 0.6 s of processing time on regular personal computer for each second of multichannel surface EMG.

Regularized Latent Semantic Indexing

Topic modeling provides a powerful way to analyze the content of a collection of documents. It has become a popular tool in many research areas, such as text mining, information retrieval, natural language processing, and other related fields. In real-world applications, however, the usefulness of topic modeling is limited due to scalability issues. Scaling to larger document collections via parallelization is an active area of research, but most solutions require drastic steps, such as vastly reducing input vocabulary. In this article we introduce Regularized Latent Semantic Indexing (RLSI)---including a batch version and an online version, referred to as batch RLSI and online RLSI, respectively---to scale up topic modeling. Batch RLSI and online RLSI are as effective as existing topic modeling techniques and can scale to larger datasets without reducing input vocabulary. Moreover, online RLSI can be applied to stream data and can capture the dynamic evolution of topics. Both versions of RLSI formalize topic modeling as a problem of minimizing a quadratic loss function regularized by ℓ1 and/or ℓ2 norm. This formulation allows the learning process to be decomposed into multiple suboptimization problems which can be optimized in parallel, for example, via MapReduce. We particularly propose adopting ℓ1 norm on topics and ℓ2 norm on document representations to create a model with compact and readable topics and which is useful for retrieval. In learning, batch RLSI processes all the documents in the collection as a whole, while online RLSI processes the documents in the collection one by one. We also prove the convergence of the learning of online RLSI. Relevance ranking experiments on three TREC datasets show that batch RLSI and online RLSI perform better than LSI, PLSI, LDA, and NMF, and the improvements are sometimes statistically significant. Experiments on a Web dataset containing about 1.6 million documents and 7 million terms, demonstrate a similar boost in performance.

A self-organization based optical flow estimator with GPU implementation

This work describes a parallelizable optical flow field estimator based upon a modified batch version of the Self-Organizing Map (SOM). This estimator handles the ill-posedness in gradient-based motion estimation via a novel combination of regression and self-organization. The aperture problem is treated using an algebraic framework that partitions motion estimates obtained from regression into two sets: one (set Hc) containing motion estimates with high confidence and another (set Hp) with low confidence. The self-organization step uses a uniquely designed pair of sets: training set (Q = Hc) and the initial weights set (\({W=H_c \cup H_p}\)). It is shown that with this specific choice of training and initial weights sets, the interpolation of flow vectors is achieved primarily due to the regularization property of the SOM. Moreover, the computationally involved step of finding the winner unit in SOM simplifies to indexing into a 2D array making the algorithm highly scalable. To preserve flow discontinuities at occlusion boundaries, we have designed an anisotropic neighborhood function for SOM that uses a novel optical flow constraint equation residual-based distance measure. A multi-resolution or pyramidal approach is used to estimate large motion. As self-organization based motion estimation is computationally intense, parallel processing on graphics processing units is used for speedup. As the algorithm is data (rather datum) parallel, with sufficient number of computing cores, the implementation of the estimator can be made real time. With the available ground truth from Middlebury database, error metrics like average angular error and average end point error are computed and are shown to be comparable with other leading techniques.

Batch sequential design to achieve predictive maturity with calibrated computer models

Sequential experiment design strategies have been proposed for efficiently augmenting initial designs to solve many problems of interest to computer experimenters, including optimization, contour and threshold estimation, and global prediction. We focus on batch sequential design strategies for achieving maturity in global prediction of discrepancy inferred from computer model calibration. Predictive maturity focuses on adding field experiments to efficiently improve discrepancy inference. Several design criteria are extended to allow batch augmentation, including integrated and maximum mean square error, maximum entropy, and two expected improvement criteria. In addition, batch versions of maximin distance and weighted distance criteria are developed. Two batch optimization algorithms are considered: modified Fedorov exchange and a binning methodology motivated by optimizing augmented fractional factorial skeleton designs.

Mismatched filter for analogue TV-based passive bistatic radar

This study examines the problem of solving the range ambiguity in analogue TV (ATV)-based passive bistatic radar (PBR). When matched filter (MF) is applied to ATV-based PBR, a range ambiguity at 19.2 km will be produced because of the 64 µs line fly-back time of the ATV signal. The relatively short range and the severity of this ambiguity make application of this type of waveform much more likely to be limited. A novel mismatched filter (MMF) algorithm is developed for solving the problem of range ambiguity. In this new MMF algorithm, the MMF factor is acquired based on minimising the overall impact of the SNR loss (SL) compared to MF and the range sidelobes energy that the authors want to suppress. And a batch version with much computational saving is also introduced. Then the performance of this batch version is studied in detail. Finally, its effectiveness is demonstrated with the application to real data acquired with an experimental PBR system.

I/O-efficient batched union-find and its applications to terrain analysis

In this article we present an I/O-efficient algorithm for the batched (off-line) version of the union-find problem. Given any sequence of N union and find operations, where each union operation joins two distinct sets, our algorithm uses O (SORT( N )) = O ( N / B log M/B N / B ) I/Os, where M is the memory size and B is the disk block size. This bound is asymptotically optimal in the worst case. If there are union operations that join a set with itself, our algorithm uses O (SORT( N ) + MST( N )) I/Os, where MST( N ) is the number of I/Os needed to compute the minimum spanning tree of a graph with N edges. We also describe a simple and practical O (SORT( N ) log( N / M ))-I/O algorithm for this problem, which we have implemented. We are interested in the union-find problem because of its applications in terrain analysis. A terrain can be abstracted as a height function defined over R 2 , and many problems that deal with such functions require a union-find data structure. With the emergence of modern mapping technologies, huge amount of elevation data is being generated that is too large to fit in memory, thus I/O-efficient algorithms are needed to process this data efficiently. In this article, we study two terrain-analysis problems that benefit from a union-find data structure: (i) computing topological persistence and (ii) constructing the contour tree. We give the first O (SORT( N ))-I/O algorithms for these two problems, assuming that the input terrain is represented as a triangular mesh with N vertices.

Unbiased Adaptive Estimations of the Fourth-Order Cumulant for Real Random Zero-Mean Signal

In this paper, a consistent efficient estimator of the fourth-order cumulant for real discrete-time random i.i.d. (at least up to order 8) zero-mean signal is proposed, in both, batch and adaptive versions. In batch version, the proposed estimator is not only consistent, but also unbiased and efficient. The systematical theoretical and experimental studies with comparisons between the proposed estimator and three other estimators of the fourth-order cumulant (the natural or the traditional one, the trivial unbiased estimator for the known power case and the fourth k -statistics), are undertaken, for both, normal and uniform processes. Then, the adaptive versions of the estimators (all, except the fourth k-statistics), are given and studied in detail. The convergence in mean and the convergence in mean square analyses are performed for them, first theoretically, then empirically. Finally, the whole set of analyses carried out for both batch and adaptive versions shows that from many points of view the proposed estimator is interesting for use in versatile signal processing applications, especially in real-time and short-term ones.