nils-herrmann/clean_scirepeval huggingface dataset:
Multiresolution segmentation of respiratory electromyographic signals

Analysis of respiratory electromyographic (EMG) signals in the study of respiratory control requires the detection of burst activity from background (signal segmentation), and focuses upon the determination of onset and cessation points of the burst activity (boundary estimation). The authors describe a new automated multiresolution technique for signal segmentation and boundary estimation. During signal segmentation, a new transitional segment is defined which contains the boundary between background a burst activity. Boundary estimation is then performed within this transitional segment. Boundary candidates are selected and a probability is attributed to each candidate, using an artificial neural network. The final boundary for a given transitional segment is the boundary estimate with the maximum a posteriori probability. This new method has proved accurate when compared to boundaries chosen by two investigators.<<ETX>>
The effect of filtering in the EEG correlation dimension estimation: experimental results

The effect of linear filtering on the estimation of the correlation dimension of the electroencephalogram (EEG) is described. Three types of filters and several bandwidths are applied to the signal and to white noise sequences, in order to measure experimentally the effect in the correlation dimension. The results show that a reduction in bandwidth does affect the correlation exponent map of white noise sequences, so that the usual way of displaying the correlation dimension, comparing it to the theoretically straight line of the white noise, is misleading. It is suggested that the plot of the correlation exponent (of the EEG or any other bandlimited signal) should be graphed with the correlation exponent of white noise for the same measurement bandwidth.<<ETX>>
Identification of dynamical systems using GMM with VQ initialization

We are using Gaussian mixture models (GMM) as a tool to construct local mappings of nonlinear multi-input multi-output (MIMO) systems. In this work, we combine the advantages of GMM with the Kalman filter. To improve the accuracy of the local linear mappings in a potentially large dimensional state space, we propose to initialize the GMM parameters with vector quantization (VQ) or its more parsimonious counterpart growing self-organizing maps (G-SOM). The performance of the proposed modeling algorithm on simulated data obtained from a realistic aircraft model show improvements in both converge speed and accuracy.
Extracting an evaluative feedback from the brain for adaptation of motor neuroprosthetic decoders

The design of Brain-Machine Interface (BMI) neural decoders that have robust performance in changing environments encountered in daily life activity is a challenging problem. One solution to this problem is the design of neural decoders that are able to assist and adapt to the user by participating in their perception-action-reward cycle (PARC). Using inspiration both from artificial intelligence and neurobiology reinforcement learning theories, we have designed a novel decoding architecture that enables a symbiotic relationship between the user and an Intelligent Assistant (IA). By tapping into the motor and reward centers in the brain, the IA adapts the process of decoding neural motor commands into prosthetic actions based on the user's goals. The focus of this paper is on extraction of goal information directly from the brain and making it accessible to the IA as an evaluative feedback for adaptation. We have recorded the neural activity of the Nucleus Accumbens in behaving rats during a reaching task. The peri-event time histograms demonstrate a rich representation of the reward prediction in this subcortical structure that can be modeled on a single trial basis as a scalar evaluative feedback with high precision.
Knowledge representation in machine tool supervision systems

A deeply coupled numeric-symbolic machine tool supervision system for tool breakage detection is developed. The supervision system hierarchically integrates real-time signal-processing algorithms in a knowledge-based processing environment where rules and objects coexist. A numeric-symbolic model that incorporates physical models and empirical knowledge is developed. An application to tool damage detection in milling is described, with emphasis on the integration of symbolic and numeric processing. The decision strategy for this problem is described, and a validation study is presented.<<ETX>>
Supervised synaptic weight adaptation for a spiking neuron

A novel algorithm named Spike-LMS is described that adapts the synaptic weights of an artificial spiking neuron to produce a desired response. The derivation of Spike-LMS follows from the derivation of the least-mean squares (LMS) algorithm used in adaptive filter theory. Spike-LMS works directly in the domain of spike trains, and therefore makes no assumptions about any particular neural encoding method. This algorithm is able to identify the synaptic weights of a spiking neuron given the pre-synaptic and post-synaptic spike trains.
Undersea LiDAR imager for unobtrusive and eye safe marine wildlife detection and classification

Marine hydrokinetic (MHK) projects are composed of undersea power generating equipment that converts energy of waves, tides, or ocean currents into electricity. Of primary interest when deploying MHK devices is gaining an understanding of potential harmful interactions between marine animals and equipment at proposed development sites. Therefore, a high priority regulatory expectation exists to observe marine life interaction with such devices. Underwater video observation of MHK scenes is typically accomplished with optical or acoustical cameras. Traditional optical cameras are most effective when significant ambient light is present and in low turbidity. Even the most sophisticated commercially-available underwater camera technologies require artificial white light to illuminate low light scenes. This approach is not desirable for MHK monitoring because artificial light can alter the behavior of the animals being monitored. For example, it has been observed that marine life are attracted to light-emitting sources of wavelengths within their visual light frequency range. However, unlike active acoustic solutions, the primary advantage of using optical approaches is high resolution contrasted scene descriptions essential for object classification and detailed observations. Because red laser illuminators can be configured below the maximum permissible exposure (MPE) limit for humans, which is conservative with regard to marine wildlife due to the lower visual acuity of the eye, and also beyond the wavelength range that is visible to marine wildlife, such systems can be designed to be eye-safe, unobtrusive, and allow for 24/7 operations.
Equivalence between RLS algorithms and the ridge regression technique

Recursive implementations of the least squares algorithm start the computation with a known set of initial conditions. The information contained in new data samples is then used to update the old estimates. Therefore the initialization procedure is seen to be an integral part of recursive algorithms. A number of methods for initializing recursive least squares (RLS) algorithms have been proposed in the literature. The two most common methods are the fast exact initialization and the soft constrained initialization. This paper discusses the equivalence relationship between RLS algorithms that use soft constrained initialization and a widely used technique in statistics called "ridge regression".
Arm Motion Reconstruction via Feature Clustering in Joint Angle Space

We hypothesize that a set of movements can be used to reconstruct biomechanically realistic movements. Using parameters from a reaching and grasping task we create a representative three-dimensional motion. From this motion we extract features from the joint angle space. We believe that the physiological importance of these features makes them worth investigating as possible movements. Machine learning techniques are employed to cluster similar features. The clusters are then used to recursively reconstruct the motion trajectory. Even with only twenty clusters, the average trajectory reconstruction error in Cartesian space is less than 1% of the dynamic range of motion. Our ability to create and analyze realistic motions may be crucial to both future BMI experiments where a desired signal is not available and our understanding of motor control.
Transfer Learning in Adaptive Filters: The Nearest Instance Centroid-Estimation Kernel Least-Mean-Square Algorithm

We propose a novel nearest-neighbors approach to organize and curb the growth of radial basis function network in kernel adaptive filtering (KAF). The nearest-instance-centroid-estimation (NICE) kernel least-mean-square (KLMS) algorithm provides an appropriate time-space tradeoff with good performance. Its centers in the input/feature space are organized by quasi-orthogonal regions for greatly simplified filter evaluation. Instead of using all centers to evaluate/update the function approximation at every new point, a linear search among the iteratively-updated centroids determines the partial function to be used, naturally forming locally-supported partial functionals. Under this framework, partial functionals that compose the adaptive filter are quickly stored/retrieved based on input, each corresponding to a specialized “spatial-band” subfilter. The filter evaluation becomes the update of one of the subfilters, creating a content addressable filter bank (CAFB). This CAFB is incrementally updated for new signal applications with mild constraints, always using the past-learned partial filter sums, opening the door for transfer learning and significant efficiency for new data scenarios, avoiding training from scratch as have been done since the invention of adaptive filtering. Using energy conservation relation, we show the sufficient condition for mean square convergence of the NICE-KLMS algorithm and establish the upper and lower bounds of steady-state excess-mean-square-error (EMSE). Simulations on chaotic time-series prediction demonstrate similar levels of accuracy as existing methods, but with much faster computation involving fewer input samples. Simulations on transfer learning using both synthetic and real-world data demonstrate that NICE CAFB can leverage previously learned knowledge to related task or domain.
The effects of interictal spikes on single neuron firing patterns in the hippocampus during the development of temporal lobe epilepsy

The interictal spikes (IS) are characteristic signatures of epileptic tissue, and relevant in presurgical evaluation of epilepsy. However, the mechanism and effects of IS remain unclear. In this study, we examined the relationship between IS and single neuron firing patterns, using an epileptic rat with temporal lobe epilepsy (TLE). We investigated the firing of interneurons and pyramidal cells in the CA3 region of the hippocampus. The results show that IS are associated with decreased single neuron firing rates compared with IS-free epochs. Furthermore, the paroxysmal interictal-spiking patterns are associated with sustained decrease in single neuron firing rates. We also found that IS stopped as approaching to seizures. These results demonstrate that IS might be responsible for the development of TLE and ictal events by changing the firing patterns of hippocampal neurons.
A variable step-size adaptive algorithm under maximum correntropy criterion

Correntropy, a novel localized similarity measure defined in kernel space, has been successfully used as a cost function in adaptive system training. The adaptive algorithms under the maximum correntropy criterion (MCC) have been shown to be robust to impulsive non-Gaussian noises. However, they may converge slowly especially at a region far from the optimal solution. In this paper, we propose a new MCC algorithm with a variable step-size (VSS) called the VSS-MCC algorithm, which may achieve a much faster convergence speed while maintaining similar steady-state performance. In the new algorithm, the step-size is updated based on an approximation for the curvature of performance surface. Simulation results demonstrate the superior performance of VSS-MCC compared with the original MCC algorithm.
An integrated recording system using an asynchronous pulse representation

A neuronal recording system for brain-machine interfaces (BMI) based on asynchronous biphasic pulse coding is described. It demonstrates the first step in the development of a complete implanted wireless solution with fully integrated circuit architecture. A recording experiment comparing in parallel a commercial recording system (Tucker-Davis Technology (TDT)) and the UFs custom solution (FWIRE) is set up to compare performance. The novel aspect of the UF system is that the analog signal is represented by an asynchronous pulse train, which provides a low-power, low-bandwidth, noise-resistant means for coding and transmission. Taking advantage of neural firing features, the pulse-based approach uses only 3K pulses/second to record a 25 kHz bandwidth signal from a hardware neural simulator.
Identification of aircraft dynamics using a SOM and local linear models

The self-organizing map (SOM) is a powerful tool to produce topology preserving subspace mappings of high dimensional data. In this work we combine a SOM with a set of local linear models to implement functional mappings and identify potentially nonlinear plants. The large dimensionality of the spaces involved (many degrees of freedom and large dynamic range of parameters) is an issue, hence we compare fixed versus growing SOM topologies. The performance of the proposed algorithms is tested on the simulated data obtained from a realistic aircraft model.
Parallel flow in Deep Predictive Coding Networks

This paper proposes a cognitive architecture for sensory processing of multimodal data. The cognitive architectures, referred to as Deep Predictive Coding Networks (DPCN) were first used to model video streams. Here we use DPCNs with two input sources, for example: video and speech recordings. We train DPCNs as generative models of both sensors. Since we constrain the network to have a single hidden code for both inputs, we name the proposed architecture as Multimodal DPCN (MDPCN). Experimental results show that the “parallel” flow between the two sensory modes increases the interclass separability achieved by unsupervised clustering. We validate the proposed method with a multimodal classification task using part of the VIDTIMIT dataset.
Discrete network implementation of Freeman's model of the olfactory system

This paper presents results concerning a full digital implementation of the KII and KIII model proposed by Freeman to explain the chaotic behavior of the mammalian olfactory cortex (1995). The environment used for the task is a commercial neural network package. The immediate goal is to offer a computational efficient alternative to Runge-Kutta methods of solving the system's differential equations. To accomplish the goal the differential equations' impulse response were sampled and decomposed over the gamma basis. Both models, KII and KIII, are presently working as associative memories, where stored patterns can be recalled from an input pattern.
An actor-critic architecture and simulator for goal-directed brain-machine interfaces

The Perception-Action Cycle (PAC) is a central component of goal-directed behavior because it links internal percepts with external outcomes in the environment. Using inspiration from the PAC, we are developing a Brain-Machine Interface control architecture that utilizes both motor commands and goal information directly from the brain to navigate to novel targets in an environment. An Actor-Critic algorithm was selected for decoding the neural motor commands because it is a PAC-based computational framework where the perception component is implemented in the critic structure and the actor is responsible for taking actions. We develop in this work a biologically realistic simulator to analyze the performance of the decoder in terms of convergence and target acquisition. Experience from the simulator will guide parameter selection and assist in understanding the architecture before animal experiments. By varying the signal to noise ratio of the neural input and error signal, we were able to demonstrate how the learning rate and initial conditions affect a motor control target selection task. In this framework, the naïve decoder was able to reach targets in the presence of noise in the error signal and neural motor command with 98% accuracy.
A New Spatiotemporal Filtering Method for Single-Trial Estimation of Correlated ERP Subcomponents

A novel spatiotemporal filtering method for single trial estimation of event-related potential (ERP) subcomponents is proposed here. Unlike some previous works in ERP estimation, the proposed method is able to estimate temporally correlated ERP subcomponents such as P3a and P3b. A new cost function is, therefore, defined which can deflate one of the correlated subcomponents. The method is applied to both simulated and real data and has shown to perform very well even in low signal-to-noise ratio situations. In addition, the method is compared to spatial principal component analysis and its superiority has been confirmed by using simulated signals. The approach can be especially useful in mental fatigue analysis where the relative variability of P300 subcomponents is the key factor in detecting the level of fatigue.
Bimodal brain-machine interface for motor control of robotic prosthetic

We are working on mapping multi-channel neural spike data, recorded from multiple cortical areas of an owl monkey, to corresponding 3D monkey arm positions. In earlier work on this mapping task, we observed that continuous function approximators (such as artificial neural networks) have difficulty in jointly estimating 3D arm positions for two distinct cases-namely, when the monkey's arm is stationary and when it is moving. Therefore, we propose a multiple-model approach that first classifies neural spike data into two classes, corresponding to two states of the monkey's arm: (1) stationary and (2) moving. Then, the output of this classifier is used as a gating mechanism for subsequent continuous models, with one model per class. In this paper, we first motivate and discuss our approach. Next, we present encouraging results for the classifier stage, based on hidden Markov models (HMMs), and also for the entire bimodal mapping system. Finally, we conclude with a discussion of the results and suggest future avenues of research.
A knowledge-based supervision model for machine tools

The knowledge-based supervision system described is intended to detect cutter damages in milling machines, using x-axis and y-axis displacement signals. The model hierarchically integrates real-time signal processing algorithms in a knowledge-based processing environment where rules and objects coexist. A deeply coupled, numeric/symbolic model is developed. It incorporates physical models and empirical knowledge. It is implemented in a multiprocessor architecture.<<ETX>>
From linear adaptive filtering to nonlinear information processing - The design and analysis of information processing systems

Recent advances in computing capabilities and the interest in new challenging signal processing problems that cannot be successfully solved using traditional techniques have sparked an interest in information-theoretic signal processing techniques. Adaptive nonlinear filters that process signals based on their information content have become a major focus of interest. The design and analysis of such nonlinear information processing systems is demonstrated in this paper. Theoretical background on necessary information theoretic concepts are provided, nonparametric sample estimators for these quantities are derived and discussed, the use of these estimators for various statistical signal processing problems have been illustrated. These include data density modeling, system identification, blind source separation, dimensionality reduction, image registration, and data clustering
A low power battery management system for rechargeable wireless implantable electronics

An integrated battery management system for low power wireless implantable electronics is presented herein. The system is designed for miniature implantable Li-ion rechargeable batteries with limited cell capacity and employs a new control loop that relaxes comparator resolution requirements, provides simultaneous operation of constant-current and constant voltage loops, and eliminates the external current sense resistor from the charging path. The accuracy of the end-of-charge detection is primarily determined by the voltage drop across matched resistors and current-sources and the offset voltage of the sense comparator. Preliminary simulations in 0.5mum bulk CMOS technology indicate that plusmn1% (or plusmn15muA) end-of-charge accuracy can be obtained under worst-case conditions for a comparator offset voltage of plusmn5mV. The proposed battery control loop measures roughly 170mum by 960mum and dissipates approximately 150muWatts
A Portable Wireless DSP System for a Brain Machine Interface

In this paper, we present a design for a wearable DSP system that is capable of processing various neural-to-motor translation algorithms. The system first acquires the neural data through a high speed data bus in order to train and evaluate our prediction models. Then via a widely used protocol, the low-bandwidth output trajectory is wirelessly transmitted to a simulated robot arm. This system has been built and successfully tested with real data
Computational Intelligence Challenges and Applications on Large-Scale Astronomical Time Series Databases

Time-domain astronomy (TDA) is facing a paradigm shift caused by the exponential growth of the sample size, data complexity and data generation rates of new astronomical sky surveys. For example, the Large Synoptic Survey Telescope (LSST), which will begin operations in northern Chile in 2022, will generate a nearly 150 Petabyte imaging dataset of the southern hemisphere sky. The LSST will stream data at rates of 2 Terabytes per hour, effectively capturing an unprecedented movie of the sky. The LSST is expected not only to improve our understanding of time-varying astrophysical objects, but also to reveal a plethora of yet unknown faint and fast-varying phenomena. To cope with a change of paradigm to data-driven astronomy, the fields of astroinformatics and astrostatistics have been created recently. The new data-oriented paradigms for astronomy combine statistics, data mining, knowledge discovery, machine learning and computational intelligence, in order to provide the automated and robust methods needed for the rapid detection and classification of known astrophysical objects as well as the unsupervised characterization of novel phenomena. In this article we present an overview of machine learning and computational intelligence applications to TDA. Future big data challenges and new lines of research in TDA, focusing on the LSST, are identified and discussed from the viewpoint of computational intelligence/machine learning. Interdisciplinary collaboration will be required to cope with the challenges posed by the deluge of astronomical data coming from the LSST.