nils-herrmann/clean_scirepeval huggingface dataset:
Spatial proximity based subspace decomposition for movement direction decoding of Local Field Potentials

Local Field Potentials (LFP) provides higher spatial resolution and SNR than EEG data and can be used to construct a Brain Computer Interface. In , we have shown that movement direction decoding can be done with about 90 % classification accuracy using spatial patterns (CSP) and Error Correction Output Codes (ECOC). However, a major challenge in this study is to make this method more robust to inter-session variability of the LFP data, where state-of-the-art results are in the high 70 percent. In , we have demonstrated that LFP features that are recurrent across sessions can be extracted using a subspace learning method and used to improve the CSP +ECOC classifier. In this work, we propose an extension of the subspace learning method that exploits the spatial topology of the channels. This allows us to learn spatially diverse features, while previously the subspaces were being learned independently of the channel layout. We proposed a method where a block of samples from neighboring channels is used to find the subspaces and decode the directions. This approach is analogous to analyzing an 8x8 pixel map in image processing. Furthermore, this method allows a spatio-temporal classification, and it is indeed observed that different directions were providing higher accuracies at different time blocks. The proposed method can boosts the accuracy by at least 6% to bring classification to the mid 80 percent. Furthermore, we show early results where adding a pilot trial from the test session can be used as a calibration to further improve the spatio-temporal classification.
Adaptive low power multimedia wireless communications

In this paper, we introduce a novel approach for adaptive minimization of the total energy consumption in multimedia wireless communications subject to achieving a given quality of service. Our approach exploits trade-offs between the effects of energy consumed in processing (source and channel coding) and energy consumed in transmission under different noise and channel conditions, on the received quality of the multimedia. We present several simulation results involving image transmission that illustrate the energy consumption savings that can be achieved using our proposed approach.
Real-time data selection and ordering for cognitive bias mitigation

We consider the problem of selecting and ordering a subset of N' out of N observations to be presented to a human being in the context of a binary hypothesis testing problem. We restrict our attention to i.i.d. Gaussian observations. We propose an extension of the approximate subset sum algorithm, and show that it can be used to solve the problem with polynomial complexity. Furthermore, we show that the solution yields near optimal detection performance when compared to the case where all N observations are optimally processed.
Identifying patterns of copy number variants in case-control studies of human genetic disorders

DNA copy number variations are now recognized as an important contributor to human genetic disease. In this paper, our focus is on identifying patterns of DNA copy number variation detected with finely-tiled oligonucleotide arrays in case-control studies. This analysis is based on the observation that CNVs across large segments of the genome show recurring patterns, particularly in regions that bear repeat sequences that contribute to the genetic instability of that interval. The goal of this analysis is to increase the power to identify disease-associated genetic changes in case-controls studies of copy number variation. We propose a framework to evaluate the predictive power of recurrent variations at multiple genomic sites. First, we present a novel method based on maximum likelihood principle to clearly map and detect copy number variations along the studied genomic segments. Second, we apply regional analysis to evaluate the statistical and biological significance of recurrent variations followed by clustering methods to classify the tested samples. Finally, our results show that using the concatenated recurrent variant regions will considerably increase classification performance when compared with the traditional classifiers that use the entire data set. The results also provide insight into the pattern of the variations that may have a direct role in the targeted disease and can be used to improve diagnostic reliability for complex human genetic disorders.
Wavelet transform domain RLS algorithm

This paper describes a new wavelet domain RLS algorithm. The algorithm exploits the special sparse structure of the wavelet transform of wide classes of correlation matrices and that of the Cholesky factors of these matrices. Specifically, the algorithm updates a sparse QR factorization of the wavelet domain input data matrix. It then uses these factors to obtain the least-squares (LS) filter coefficients. The computational complexity of the proposed method is O(M log(M)) flops even when the input signal is not a time-series. Its convergence performance is similar to the traditional RLS algorithm. The new algorithm provides a method for trading error performance for lower computational complexity. Simulation results validate the performance of the algorithm.
Movement direction decoding with spatial patterns of local field potentials

We show that movement direction can be decoded with high accuracy using the spatial patterns extracted from multichannel local field potentials (LFPs). Two monkeys were trained to execute center-out movement in 8 directions. During the task the LFP activity was recorded with two 64 channel grids from the pre- and primary motor areas. The LFP signals were decomposed into 4 sub-band components in the 0–4 Hz, 4–10 Hz, 14–30 Hz and 48–200 Hz frequency ranges. The sub-band activity was post processed with regularized common spatial patterns algorithm and fed to linear discriminant analysis for final classification. Directions of movement were estimated using a redundant hierarchical classification strategy that tested groups of directions against diametrically opposite groups. The grouping of directions was based on the spatial correlation that we observed between LFP signals corresponding to neighboring movement directions which is similar to the cosine tuning profile of single neurons. We found that the decoding power for 8 directions was 80% and 92% for the two subjects, respectively, in 0–4Hz frequency band. Our best result of 92% nearly doubles the accuracy of the best results reported in the literature with similar set-ups. These results indicate that spatial patterns in LFP can be used to construct high accuracy brain computer interfaces.
Channel Idle Time Statistics Based Spectrum Accessing Strategies With CSMA Based Primary Networks

Channel statistics based secondary transmission strategy design has been studied intensively in the past. In this work, we consider the same problem but with a carrier sensing multiple access based primary networks, i.e., the primary users that would react to interference. In such primary networks, users always need to perform carrier sensing before accessing the channel. Such networks include 802.11 WLAN and 802.15 WPAN. The secondary transmission strategy design when the primary network uses carrier sensing based network access differs a lot from what has been considered in the literature. Here, we show through experiments with 802.11 networks that under secondary interference, the primary users can experience not only packet collisions but also transmission delay. Under long-term secondary interference, the primary users might even choose to transmit in another channel. This phenomenon is known as the channel capture effect. Next, with this in mind, we formulate a novel transmission strategy design problem that aims to maximize secondary transmission time with design constraints on the probability of packet collision or delay, as well as the probability that the primary user switches channel. Finally, we describe a software-defined radio device that we built to execute the designed transmission strategies on the 2.4 GHz band. The performance of both the primary and secondary users are studied. Results show that our consideration for the primary user reactions is necessary. In particular, the designed transmission strategy better protects the primary user transmission while maintaining secondary user performance.
Audio-visual content-based violent scene characterization

We present a novel technique to characterize and index violent scenes in general TV drama and movies. Our goal is to identify violent signatures and localize violent events within a movie to support "high-level" video indexing. In particular, we exploit multiple "audio-visual" signatures to create a perceptual relation for conceptually meaningful violent scene identification. Potential applications are automatic blocking of violence in movies watched by children, hiding violence using data hiding or information filtering and genre classification of digital video database.
Maximum likelihood estimation of the parameters of discrete fractionally differenced Gaussian noise process

A maximum-likelihood estimation procedure is constructed for estimating the parameters of discrete fractionally differenced Gaussian noise from an observation set of finite size N. The procedure does not involve the computation of any matrix inverse or determinant. It requires N/sup 2//2+O(N) operations. The expected value of the loglikelihood function for estimating the parameter d of fractionally differenced Gaussian noise (which corresponds to a parameter of the equivalent continuous-time fractional Brownian motion related to its fractal dimension) is shown to have a unique maximum that occurs at the true value of d. A Cramer-Rao bound on the variance of any unbiased estimate of d obtained from a finite-sized observation set is derived. It is shown experimentally that the maximum-likelihood estimate of d is unbiased and efficient when finite-size data sets are used in the estimation procedure. The proposed procedure is extended to deal with noisy observations of discrete fractionally differenced Gaussian noise.
Framework for the Identification of Common Variations in Multiple DNA Copy Number Samples

diseases such as cancer and autism. Microarray techniques are used to detect copy number variations with high- resolution. However, the observed copy numbers are corrupted by noise, making variations breakpoints hard to detect. Various techniques had been proposed to uncover the true copy number changes. In this study, we provide a framework for the analysis of copy number datasets. It is divided into two parts: The sigma filter as pre-processing technique, and statistical models for classifying nonrandom variations across multiple samples. Finally, we compared our results with reported variations in real samples.
Analysis and Visualization of Movement related EEG activities with Local Discriminant Bases

We use local cosine packets to adaptively segment EEG corresponding to left or right hand index finger movements. The segmentation is constructed by maximizing the Euclidean or Kullback-Leibler distance criterion between the left and right finger movement. The proposed method divides the movement EEG from the C3 and C4 electrodes into nonuniform time segments over a dyadic tree. The most discriminative features are selected from the pruned tree. We observed that the selected segmentation and discriminative components are subject specific. We believe this may eliminate the inter and intrasubject variability when constructing brain computer interfaces. We also found striking asymmetry between feature characteristics and their discrimination power on each hemisphere
Kalman estimation for a class of rational isotropic random fields

This paper considers the application of Kalman estimation theory to the problem of estimating two-dimensional isotropic random fields, whose equations are expressed in terms of the Laplacian, given some noisy observations on a finite disk. It is shown that this problem is equivalent to that of solving a countably infinite number of one-dimensional estimation problems. Markovian models for the one-dimensional processes are developed and the associated Kalman filters are shown to be asymptotically stable. The desired field estimate is then obtained by combining the smoothed estimates resulting from each of the one-dimensional problems weighted in an appropriate fashion.
Localization in wireless sensor networks under non line-of-sight propagation

This paper addresses ranging in indoor quasi-static sensor environments and presents a time-of-arrival (TOA) based ranging algorithm. A statistical model of the multipath channel in the form of the signal return and noise characterization is derived, and utilized to distinguish signal components from noise. The algorithm then uses multiple signal receptions at each base station, to differentiate between line-of-sight (LOS) and non-LOS components, and to accurately estimate the position of the LOS component in the received multipath signal. The location is estimated through a mathematical programming problem formulation. Using a synthesized bandwidth of 2 GHz, a 4-bit analog-to-digital converter (ADC) and with 5-10 dB signal-to-noise ratio (SNR), range estimation with sub-meter accuracy is achieved. Furthermore, the associated range estimation error does not increase with increase in the transmitter-receiver range
Parallel identification of gene biclusters with coherent evolutions

Finding clusters of genes with expression levels that evolve coherently under subsets of conditions can help uncover genetic pathways. This can be done by applying a biclustering procedure to gene expression data. Given a microarray data set with M genes and N conditions, we define a bicluster with coherent evolution as a subset of genes with expression levels that are nondecreasing as a function of a particular ordered subset of conditions. We propose a new biclustering procedure that identifies all biclusters with a specified number of K conditions in parallel with O(MK) complexity. Unlike almost all prior biclustering techniques, the proposed approach is guaranteed to find all biclusters with a specified minimum numbers of genes and conditions in the data set. All of the biclusters it identifies have no imperfection, i.e., the evolutions of the genes in each bicluster will be coherent across all conditions in the bicluster. Furthermore, the complexity of the proposed approach is lower than that of prior approaches.
Selection of spectro-temporal patterns in multichannel MEG with support vector machines for schizophrenia classification

We present a new framework for the diagnosis of schizophrenia based on the spectro-temporal patterns selected by a support vector machine from multichannel magnetoencephalogram (MEG) recordings in a verbal working memory task. In the experimental paradigm, five letters appearing sequentially on a screen were memorized by subjects. The letters constituted a word in one condition and a pronounceable nonword in the other. Power changes were extracted as features in frequency subbands of 248 channel MEG data to form a rich feature dictionary. A support vector machine has been used to select a small subset of features with recursive feature elimination technique (SVM-RFE) and the reduced subset was used for classification. We note that the discrimination between patients and controls in the word condition was higher than in the non-word condition (91.8% vs 83.8%). Furthermore, in the word condition, the most discriminant patterns were extracted in delta (1–4 Hz), theta (4–8Hz) and alpha (12–16 Hz) frequency bands. We note that these features were located around the left frontal, left temporal and occipital areas, respectively. Our results indicate that the proposed approach can quantify discriminative neural patterns associated to a functional task in spatial, spectral and temporal domain. Moreover these features provide interpretable information to the medical expert about physiological basis of the illness and can be effectively used as a biometric marker to recognize schizophrenia in clinical practice.
Group-Biomarkers Identification in Ovarian Carcinoma

In this paper, we propose group-biomarkers as an alternative to the traditional single biomarkers used to date for the detection of ovarian cancer. Group-biomarkers are a set of genes that are used simultaneously for the diagnosis of early-stage and/or recurrent cancer. We describe a procedure for identifying such group-biomarkers from a data set of gene expression levels corresponding to normal and diseased ovarian tissue as well as tissue from other organs. The procedure starts with a list of potential single biomarkers. It then uses an order preserving biclustering step to identify other genes that are co-regulated with the candidate single biomarkers across the normal and diseased ovarian tissue and tissue from other organ. We present a statistical analysis that demonstrates that group-biomarkers have a much better detection performance than single biomarkers as exhibited by receiver operating characteristics curves.
Hierarchical Sequential Detection In The Context Of Dynamic Spectrum Access For Cognitive Radios

Dynamic spectrum sharing is a concept where unlicensed devices are allowed to opportunistically access unused spectrum bands. The goal of this paper is to develop and analyze a novel secondary user sensing strategy that on the average minimizes the need to take sensing measurements and therefore maximizes throughput. In particular, we focus on the IEEE 802.22 standard where the goal is to detect TV signals. A secondary or unlicensed user must continuously monitor the band it is using and relinquish it as soon as it detects a primary or licensed user. The secondary user has to minimize its probability of missed opportunity to transmit while adhering to the probability of detecting the primary user. In this paper, we have applied the methods of sequential detection to reduce the sensing time. A collaborative detection scheme with the energy detector at the secondary user running a sequential detection test is used. The central node runs a parallel sequential test. Performance gains have been quantified for hard and soft decision combining at the central node and compared with the IEEE 802.22 wireless regional area network standard. The proposed scheme delivers a 3-204 times increase in throughput for SNRs in the range -10 dB to 0 dB.
Doubly Sequential Energy Detection for Distributed Dynamic Spectrum Access

We study the distributed sequential energy detection problem in the context of spectrum sensing for cognitive radio networks. We formulate a novel Doubly Sequential Energy Detector (DSED) and provide a comprehensive study of its performance. Specifically, we present the first method that sequentially combines the decisions of the Cognitive Radio nodes wherein each node is running an independent Sequential Energy Detector (SED). Through extensive simulations it is demonstrated that (i)our novel sequential version of the energy detector delivers a significant throughput improvement of 2 to 6 times over the fixed sample size test while maintaining equivalent operating characteristics as measured by the Probabilities of Detection (PD) and False Alarm (PFA), and (ii) the Doubly Sequential Procedure at the Base Station further boosts the SED performance while improving the robustness for shadowed Cognitive Radio nodes. For example, for a PD > 0.95, our simulations demonstrate that the DSED has a PFA < 0.20 while utilizing upto 8 times fewer samples than the equivalent energy detector upto a Signal to Noise Ratio of -10 dB, below which its performance gracefully degrades.
Non-Redundant and Redundant Post Coding in OFDM Systems

Orthogonal frequency division multiplexing (OFDM) provides a viable solution to communicate over frequency selective fading channels by converting them to an equivalent collection of flat fading channels. In doing so, OFDM systems fail to reap the benefits of diversity available in multipath fading channels. To ameliorate this shortcoming of OFDM, explicit diversity in the form of redundant or non-redundant coding is needed. Our main goal in this paper is to explore different options for low complexity encoder design and compare their performance and complexity. Specifically, in the class of non-redundant codes we discuss the use of signal space diversity codes in OFDM systems. For redundant codes, we introduce a novel low complexity postcoded-OFDM system where coding is employed after performing the IFFT (inverse fast Fourier transform) in the transmitter to reduce system complexity. Simulation results show that postcoded-OFDM with redundant coding outperforms other choices of encoder design considered in this paper
Design and parameterization of M-band orthonormal wavelets

The authors generalize the parameterization of 2-band (or dyadic) wavelet to the M-band case. A complete characterization of M-band wavelets is given, including a recipe for constructing such wavelets. It is shown that M-band wavelets can be constructed from a perfect reconstruction filter bank with added regularity conditions. The regularity conditions are important both for the numerical stability of the resulting approximation procedures and for increased compactness of the signal representations with such wavelets.<<ETX>>
Time-scale invariant audio data embedding

We propose a novel algorithm for high-quality data embedding in audio. The algorithm is based on changing the relative length of the middle segment between two successive maximum and minimum peaks to embed data. Spline interpolation is used to change the lengths. To ensure smooth monotonic behavior between peaks, a hybrid orthogonal and nonorthogonal wavelet decomposition is used prior to data embedding. The possible data embedding rates are between 20 and 30 bps. However, for practical purposes, we use repetition codes, and the effective embedding data rate is around 5 bps. The algorithm is invariant after time-scale modification, time shift, and time cropping. It gives high-quality output and is robust to mp3 compression.
Design and implementation of multi-band pulsed-OFDM system for wireless personal area networks

We study the theory and implementation of pulsed orthogonal frequency division multiplexing (pulsed-OFDM) modulation. Pulsed-OFDM is an enhancement to the leading proposal to the IEEE 802.15.3a wireless personal area networks standardization effort, known as multi-band OFDM. In particular, we show in this paper that the pulsed-OFDM system has better performance than the non-pulsed system in indoor multipath channels and considerably lower complexity and power consumption. We begin by studying the, spectral characteristics of pulsed OFDM and the added degrees of diversity that it provides. Next, we discuss the design of receivers for such a system. We show that the diversity branches can be captured and demodulated by one or more fast Fourier transform (FFT). We then focus on a system for the IEEE 802.15.3a standard and derive a particularly low complexity implementation for that system. The implementation is based on carefully designed punctured convolutional codes. It also exploits the normal inefficiencies in an FFT architecture to implement the parallel FFT operations required to demodulate a full diversity pulsed OFDM with lower complexity and smaller area than the single FFT used by the non-pulsed system. We conclude by presenting realistic simulation results for the measured indoor propagation channels provided by the IEEE 802.15.3a standard.