nils-herrmann/clean_scirepeval huggingface dataset:
Kernel Learning for Dynamic Texture Synthesis

Dynamic textures (DTs) that represent moving scenes such as flames, smoke, and waves, exhibit fixed dynamics within a period of time and have been successfully modeled using linear dynamic systems (LDS). In this paper, we show that the widely used LDS model can be approximated using a principal component regression (PCR) model with the main advantage of simplicity. Furthermore, to capture the nonlinearity of training frames, we extend traditional PCR to its kernelized version and introduce kernel principal component regression (KPCR) to model and synthesize DTs. To ensure algorithm stability, we remove the standard state model and directly apply the quantized kernel least mean squares algorithm from signal processing domain to approximate the performance achieved with KPCR. We term this improvement kernel adaptive dynamic texture synthesis (KADTS), which also has the benefits of computational and memory efficiency. These advantages make KADTS ideally suited for real-world applications, since the majority of electronic devices, including cell phones and laptops, suffer from limited memory and real-time constraints. We demonstrate, via both theoretical and experimental analyses, the connections between DT synthesis using KPCR and KADTS with a regularization network theory. We also show the superiority of our proposed algorithms for DT synthesis compared with other dynamic system-based benchmarks. MATLAB code is available from our project homepage http://bmal.hust.edu.cn/project/dts.html.
Compressed signal reconstruction using the correntropy induced metric

Recovering a sparse signal from insufficient number of measurements has become a popular area of research under the name of compressed sensing or compressive sampling. The reconstruction algorithm of compressed sensing tries to find the sparsest vector (minimum lo-norm) satisfying a series of linear constraints. However, lo-norm minimization, being a NP hard problem is replaced by li-norm minimization with the cost of higher number of measurements in the sampling process. In this paper we propose to minimize an approximation of lo-norm to reduce the required number of measurements. We use the recently introduced correntropy induced metric (CIM) as an approximation of lo-norm, which is also a novel application of CIM. We show that by reducing the kernel size appropriately we can approximate the lo-norm, theoretically, with arbitary accuracy.
The Laplacian spectral classifier

We develop a novel classifier in a kernel feature space defined by the eigenspectrum of the Laplacian data matrix. The classification cost function is derived from a distance measure between probability densities. The Laplacian data matrix is obtained based on a training set, while test data is mapped to the kernel space using the Nystrom routine. In that space, the test data is classified based on the angle between the test point and the training data class means. We illustrate the performance of the new classifier on synthetic and real data.
Brain-Machine Interface Engineering

Neural interfaces are one of the most exciting emerging technologies to impact bioengineering and neuroscience because they enable an alternate communication channel linking directly the nervous system with man-made devices. This book reveals the essential engineering principles and signal processing tools for deriving control commands from bioelectric signals in large ensembles of neurons. The topics featured include analysis techniques for determining neural representation, modeling in motor systems, computing with neural spikes, and hardware implementation of neural interfaces. Beginning with an exploration of the historical developments that have led to the decoding of information from neural interfaces, this book compares the theory and performance of new neural engineering approaches for BMIs.
A global least mean square algorithm for adaptive IIR filtering

In this brief, we develop a least mean square (LMS) algorithm that converges in a statistical sense to the global minimum of the mean square error (MSE) objective function. This is accomplished by estimating the gradient as a smoothed version of the MSE, The smoothed MSE objective begins as a convex functional in the mean. The amount of dispersion or smoothing is reduced, such that over time it becomes the true MSE as the algorithm converges to the global minimum. We show that this smoothing behavior is approximated by appending a variable noise source to the infinite impulse response (IIR)-LMS algorithm. We show, experimentally, that the proposed method does converge to the global minimum in the cases tested. A performance improvement over the IIR-LMS algorithm and the Steiglitz-McBride algorithm has been achieved.
Variance and Bias Analysis of Information Potential and Symmetric Information Potential

Information theoretical learning (ITL) is a signal processing technique that goes far beyond the traditional techniques based on second order statistics which highly relies on the linearity and Gaussinarity assumptions. Information potential (IP) and symmetric information potential (SIP) are very important concepts in ITL used for system adaptation and data inference. In this paper, a mathematical analysis of the bias and the variance of their estimators is presented. Our results show that the variances decrease as the sample size N increases at the speed of O(N-1) and a bound exists for the biases. A simple numerical simulation is demonstrated to support our analysis.
Analyzing dependence structure of the human brain in response to visual stimuli

Communication between cortices mediated by deep brain structures such as the amygdala and fusiform gyrus has been suggested to explain the enhanced perception of stimuli bearing emotional content or having facial features. In this paper, we analyze the dependence structure of the relevant brain regions to assess their connectivity in response to a facial stimulus, and to discriminate it from a mock stimulus. The proposed approach treats the brain as a graphical network where vertices correspond to locations of electroencephalogram (EEG) recordings, and weights of the edges correspond to dependence values. We employ a novel measure of dependence, called generalized measure of association (GMA), due to its underlying simplicity, and compare its performance against Pearson's correlation. The performance is assessed in terms of the discriminability between the face and mock stimuli. We observe that GMA successfully exhibits higher dependence in regions that might reflect the activity of the amygdaloid complex and the right fusiform gyrus when the stimulus is face. Furthermore, the distributions of the dependence values show that GMA also achieves a better separation between face and mock, compared to correlation.
A novel measure for independent component analysis (ICA)

Measures of independence (and dependence) are fundamental in many areas of engineering and signal processing. Shannon introduced the idea of information entropy which has a sound theoretical foundation but sometimes is not easy to implement in engineering applications. In this paper, Renyi's entropy is used and a novel independence measure is proposed. When integrated with a nonparametric estimator of the probability density function (Parzen Window), the measure can be related to the "potential energy of the samples" which is easy to understand and implement. The experimental results on blind source separation confirm the theory. Although the work is preliminary, the "potential energy" method is rather general and will have many applications.
A Monte Carlo Sequential Estimation for Point Process Optimum Filtering

Adaptive filtering is normally utilized to estimate system states or outputs from continuous valued observations, and it is of limited use when the observations are discrete events. Recently a Bayesian approach to reconstruct the state from the discrete point observations has been proposed. However, it assumes the posterior density of the state given the observations is Gaussian distributed, which is in general restrictive. We propose a Monte Carlo sequential estimation methodology to estimate directly the posterior density. Sample observations are generated at each time to recursively evaluate the posterior density more accurately. The state estimation is obtained easily by collapse, i.e. by smoothing the posterior density with Gaussian kernels to estimate its mean. The algorithm is tested in a simulated neural spike train decoding experiment and reconstructs better the velocity when compared with point process adaptive filtering algorithm with the Gaussian assumption.
Adaptive blind deconvolution of linear channels using Renyi's entropy with Parzen window estimation

Blind deconvolution of linear channels is a fundamental signal processing problem that has immediate extensions to multiple-channel applications. In this paper, we investigate the suitability of a class of Parzen-window-based entropy estimates, namely Renyi's entropy, as a criterion for blind deconvolution of linear channels. Comparisons between maximum and minimum entropy approaches, as well as the effect of entropy order, equalizer length, sample size, and measurement noise on performance, will be investigated through Monte Carlo simulations. The results indicate that this nonparametric entropy estimation approach outperforms the standard Bell-Sejnowski and normalized kurtosis algorithms in blind deconvolution. In addition, the solutions using Shannon's entropy were not optimal either for super- or sub-Gaussian source densities.
An analog VLSI implementation of a multi-scale spike detection algorithm for extracellular neural recordings

This paper discusses a multi-scale neural spike detection algorithm for a low-power analog circuit implementation. The key idea is to implement wavelet decomposition and improve spike detection by independently controlling thresholds for each scale. Each thresholded scale is then combined to provide a single output indicating a spike occurrence. This spike detection algorithm shows promising results towards a robust, compact, and unsupervised low power analog spike detection circuit. A low power frontend spike detection circuit can be added to a neural amplifier and dramatically reduce the required data bandwidth for BMI applications
Autoencoders trained with relevant information: Blending Shannon and Wiener's perspectives

It is almost seventy years after the publication of Claude Shannon's “A Mathematical Theory of Communication”  and Norbert Wiener's “Extrapolation, Interpolation and Smoothing of Stationary Time Series” . The pioneering works of Shannon and Wiener lay the foundation of communication, data storage, control, and other information technologies. This paper briefly reviews Shannon and Wiener's perspectives on the problem of message transmission over noisy channel and also experimentally evaluates the feasibility of integrating these two perspectives to train autoencoders close to the information limit. To this end, the principle of relevant information (PRI) is used and validated to optimally encode input imagery in the presence of noise.
Automatic insect recognition using optical flight dynamics modeled by kernel adaptive ARMA network

Automatic insect recognition (AIR), using noninvasive methods in situ, has far-reaching implications in entomology, agriculture, and disease control and prevention. An emerging technology in computational entomology uses flight information captured by laser sensors. Current methods treat these optical signals as static patterns, rather than time series. We propose a novel approach to AIR by evaluating each insect passage as a nonstationary process involving a sequence of pseudo-acoustic frames and modeling the short-term flight dynamics using the kernel adaptive autoregressive-moving average (KAARMA) algorithm. Since flight behavior is both nonlinear and nonstationary in nature, dynamic modeling provides a general framework that fully exploits the transitional and contextual information. Results show KAARMA classifier outperforms the state-of-the-art AIR methods, using support vector machine (SVM), deep-learning autoencoder, and batch learning, in identifying Zika vector mosquito Aedes aegypti among five species of flying insects, while using significantly more efficient data representation.
Super-resolution of images based on local correlations

An adaptive two-step paradigm for the superresolution of optical images is developed in this paper. The procedure locally projects image samples onto a family of kernels that are learned from image data. First, an unsupervised feature extraction is performed on local neighborhood information from a training image. These features are then used to cluster the neighborhoods into disjoint sets for which an optimal mapping relating homologous neighborhoods across scales can be learned in a supervised manner. A super-resolved image is obtained through the convolution of a low-resolution test image with the established family of kernels. Results demonstrate the effectiveness of the approach.
Robust switching blind equalizer for wireless cognitive receivers

Kurtosis minimization has been applied for the existing blind equalization schemes but the corresponding optimization procedures are very sensitive to the channel conditions and the initial conditions. In this paper, we introduce a new cognitive receiver front-end, which includes a novel switching blind equalizer and an automatic modulation classifier. We design a switching criterion based on the kurtosis/normalized moment ratio threshold to select the better signal between the raw data and the equalized sequence. Simulations demonstrate that our proposed robust switching blind equalization scheme can significantly outperform the existing blind equalizer and would not degrade the subsequent modulation classification accuracy.
Neuronal functional connectivity dynamics in cortex: An MSC-based analysis

The activation of neural ensembles in the cortex is correlated with behavioral states and a change in neuronal functional connectivity patterns is expected. In this paper, we investigate this dynamic nature of functional connectivity in the cortex. Because of the time scale of behavior, a robust method with limited sample size is desirable. In light of this, we utilize mean square contingency (MSC) to measure the pairwise neural dependency to quantify the cortical functional connectivity. Simulation results show that MSC is more robust than cross correlation when the sample size is small. In monkey neural data test, our approach is more effective in detecting the dynamics of functional connectivity associated with the transitions between rest and movement states.
Group-Wise Point-Set Registration Based on Rényi's Second Order Entropy

In this paper, we describe a set of robust algorithms for group-wise registration using both rigid and non-rigid transformations of multiple unlabelled point-sets with no bias toward a given set. These methods mitigate the need to establish a correspondence among the point-sets by representing them as probability density functions where the registration is treated as a multiple distribution alignment. Holders and Jensens inequalities provide a notion of similarity/distance among point-sets and R&#xe9;nyis second order entropy yields a closed-form solution to the cost function and update equations. We also show that the methods can be improved by normalizing the entropy with a scale factor. These provide simple, fast and accurate algorithms to compute the spatial transformation function needed to register multiple point-sets. The algorithms are compared against two well-known methods for group-wise point-set registration. The results show an improvement in both accuracy and computational complexity.
Clustering of time series using a hierarchical linear dynamical system

The auditory cortex in the brain does effortlessly a better job of extracting information from the acoustic world than our current generation of signal processing algorithms. Abstracting the principles of the auditory cortex, the proposed architecture is based on Kalman filters with hierarchically coupled state models that stabilize the input dynamics and provide a representation space. This approach extracts information from the input and self-organizes it in the higher layers leading to an algorithm capable of clustering time series in an unsupervised manner. An important characteristic of the methodology is that it is adaptive and self-organizing, i.e. previous exposure to the acoustic input is the only requirement for learning and recognition, so there is no need of selecting the number of clusters.
Adaptive target detection in UWB images using Laguerre network

The new ultra wide band (UWB) synthetic aperture radar (SAR) promises foliage penetration capabilities due to the different phenomenology of the reflection of the target in UWB SAR. Therefore, the automated target detection algorithms must be redesigned to take advantage of the new phenomenology. Besides, the new algorithms should also work robustly in the nonstationary environment in UWB SAR. In this paper, a new adaptive generalised likelihood ratio test (GLRT) is proposed, which can detect the metallic object robustly using the resonance response in the UWB SAR scenario. The adaptive GLRT is formulated based on the linear transform of the resonance response. For practical online applications, the applied linear transform must be chosen to achieve a better representation of the signal without too much complexity. In this case, Laguerre recurrent networks is proposed to implement the linear transform, so that the online GLRT becomes feasible.
Correntropy as a Novel Measure for Nonlinearity Tests

Statistical tests have become an essential step in nonlinear system modeling due to the complexities involved in their analysis. Correntropy is a kernel-based similarity measure which includes the information of both distribution and time structure of a stochastic process. The correntropy function's capability of preserving nonlinear characteristics and high order moments makes it a suitable candidate as a statistic for determining whether a nonlinear structure exists within the system that created the observed time series. Experiments based on surrogate data methods have confirmed that correntropy can be employed as a discriminant measure for detecting nonlinear characteristics in time series.
Dynamically Repairing and Replacing Neural Networks: Using Hybrid Computational and Biological Tools

The debilitating effects of injury to the nervous system can have a profound effect on daily life activities of the injured person . In this article, we present a project overview in which we are utilizing computational and biological principles, along with simulation and experimentation, to create a realistic computational model of natural and injured sensorimotor control systems. Through the development of hybrid in silico/biological coadaptive symbiotic systems, the goal is to create new technologies that yield transformative neuroprosthetic rehabilitative solutions and a new test bed for the development of integrative medical devices for the repair and enhancement of biological systems.
Modeling and control of unknown chaotic systems via multiple models

This paper presents a multiple model based control methodology with sliding manifold for unknown chaotic systems from a finite number of measurements of the inputs and outputs. The self-organizing map (SOM) is employed to quantize the operating regions such that a local linear model is built for each region in which a sliding mode controller is designed based on the model. Switching of the controllers is done synchronously with the active local linear model that tracks the different operating conditions. It is the purpose of this work to regulate the unknown chaos to a fixed point or a stable periodic orbit. Simulations on the unknown controlled Lorenz system illustrate the efficiency of the proposed control technique
Simulation of the Freeman model of the olfactory cortex: a quantitative performance analysis for the DSP approach

It has been shown that a framework composed of digital signal processing (DSP) elements can be used to simulate and study Freeman's model of the biologically realistic olfactory cortex. In this paper, based on impulsive invariant transformation, a DSP environment has been developed corresponding to the original continuous-time dynamical system. The performance of the DSP system is quantitatively evaluated by comparing with the original system, which is implemented using the traditional Runge-Kutta integration technique. The discrete-time architecture is shown to have high performance in approximating the dynamical behavior of the original system.
"Trends" Expert Overview Sessions Revived at ICASSP 2011: Part 3 [In the Spotlight]

The breadth of topics in signal processing continues to grow, propelled by advances in digital signal processing theory as well as in new application domains. Researchers are therefore bombarded by new information in their areas of interest, but it is rather difficult to achieve a perspective of trends and advances in collateral areas. At the 2011 IEEE ICASSP in Prague, the program committee revived the idea of expert overview sessions called “Trends,” which are summary talks authored by the IEEE Signal Processing Society Technical Committees and presented by their keynote experts.
Addressing low frequency movement artifacts in EEG signal recorded during center-out reaching tasks

The successful application of noninvasive brain-computer interfaces (BCI) to neurological rehabilitation requires examination of low frequency movement artifacts and development of accurate new methods for their correction. To this end, this study applies an adaptive trend extraction method to electroencephalogram (EEG) signals recorded during active and passive center-out reaching tasks. Distinct patterns are discovered, which correlate to arm kinematics, but are shown to be largely artifactual in nature. Notably, these patterns are found to be similar to features currently used for discrimination of movement direction, indicating a necessity for caution and precise signal processing methods when utilizing low frequency content of EEG signals in such applications.
Target prescreening based on a quadratic gamma discriminator

This work presents the development, analysis and validation of a new target discrimination module for synthetic aperture radar (SAR) imagery based on an extension of gamma functions to 2-D. Using the two parameter constant false-alarm rate (CFAR) stencil as a prototype, a new stencil based on 2-D gamma functions is used to estimate the intensity of the pixel under test and its surroundings. A quadratic discriminant function is created from these estimates, which is optimally adapted with least squares in a training set of representative clutter and target chips. This discriminator is called the quadratic gamma discriminator (QGD). The combination of the CFAR and the QGD was tested in realistic SAR environments and the results show a large improvement of the false alarm rate with respect to the two-parameter CFAR, both with high resolution (1 ft) fully polarimetric SAR and with one polarization, 1 m SAR data.
Balancing exploration and exploitation in reinforcement learning using a value of information criterion

In this paper, we consider an information-theoretic approach for addressing the exploration-exploitation dilemma in reinforcement learning. We employ the value of information, a criterion that provides the optimal trade-off between the expected returns and a policy's degrees of freedom. As the degrees of freedom are reduced, an agent will exploit more than explore. As the policy degrees of freedom increase, an agent will explore more than exploit. We provide an efficient computational procedure for constructing policies using the value of information. The performance is demonstrated on a standard reinforcement learning benchmark problem.