nils-herrmann/clean_scirepeval huggingface dataset:
Performance analysis of multi-rate 802.11 WLANs under finite load and saturation conditions

In IEEE 802.11 WLANs, stations can transmit data on the channel at different data rates. The stations by default set their values to the maximum speed. As the distances between the stations and the access point increase, the transmitters start sending at lower speeds. For example, in 802.11b, wireless stations transmit at 11, 5.5, 2 and 1 Mbps. A station also changes its modulation type and transmits at a lower rate when it faces interference from the surroundings. In this paper we introduce an analytical model that includes the capability of the standard to operate at different data rates under both, finite load and saturation conditions. Many enhancements are also made in order to make the model more consistent with the standard than existing models. The proposed analysis applies to both the basic access and the RTS/CTS access mechanisms. We provide analytical expressions for the throughput gained by the stations and the average service time. We perform experiments and simulations to validate the model.
Opportunistic scheduling with quantized feedback in wireless networks

Wireless scheduling algorithm can extract multiuser diversity (MUDiv) via prioritizing the users with best current channel condition. One drawback of MUDiv is the required feedback carrying the instantiations channel rate from all active users to the access point base station. This paper shows that this feedback load is, for the most part, unjustified. To alleviate this problem, we propose an optimal discrete rate switch-based multi-user diversity system (DSMUDiv) that allows reducing the feedback load while preserving the essential of the scheme performance. We examine DSMUDiv scheme using an absolute signal-to-noise ratio (SNR)-based scheduling mechanism, assuming all users are independent identical distributed (i.i.d.). We provide a theoretical analysis of the feedback load and the spectral efficiency for the DSMUDiv scheme and compare it with the optimal (full feedback load) selective diversity scheme. Slow Rayleigh fading is assumed. Our results show a reduction in the feedback load.
Semi-supervised event detection using higher order statistics for multidimensional time series accelerometer data

In this study, we target to automatically detect stereotypical behavioral patterns (stereotypy) and self-injurious behaviors (SIB) of Autistic children which can lead to critical damages or wounds as they tend to repeatedly harm oneself. Our custom designed accelerometer based wearable sensors are placed at wrists, ankles and upper body to detect stereotypy and SIB. The analysis was done on four children diagnosed with ASD who showed repeated behaviors that involve part of the body such as flapping arms, body rocking and self-injurious behaviors such as punching their face, or hitting their legs. Our goal of detecting novel events relies on the fact that the limitation of training data and variability in the possible combination of signals and events also make it impossible to design a single algorithm to understand all events in natural setting. Therefore, a semi-supervised method to discover and track unknown events in a multidimensional sensor data rises as a very important topic in classification and detection problems. In this paper, we show how the Higher Order Statistics (HOS) features can be used to design dictionaries and to detect novel events in a multichannel time series data. We explain our methods to detect novel events in a multidimensional time series data and combine the proposed semi-supervised learning method to improve the adaptability of the system while maintaining comparable detection accuracy as the supervised method. We, compare our results to the supervised methods that we have previously developed and show that although semi-supervised method do not achieve better performance compared to supervised methods, it can efficiently find new events and anomalies in multidimensional time series data with similar performance of the supervised method. We show that our proposed method achieves recall rate of 93.3% compared to 94.1% for the supervised method studied earlier.
Schizophrenia Classification using Working Memory MEG ERD/ERS Patterns

In this paper we investigate the use of event related desynchronization (ERD) and synchronization (ERS) patterns extracted from magnetoencephalogram (MEG) in a working memory task to discriminate between controls and patients with schizophrenia. In the experimental paradigm, sequential letters appearing on a screen are memorized by subjects. In one of two conditions the letters constituted a word. The ERD and ERS patterns are extracted in the theta, alpha, beta and gamma bands from 248 electrode locations covering the whole head. We noticed that most of the ERD patterns are localized on the left frontotemporal area in both word and nonword conditions in the late memorization stage. The beta band showed the most significant difference in this cortical area between controls and schizophrenia patients. By using a decision tree, 94.7% and 87.5% classification accuracy was obtained for controls and patients individually in both word and nonword conditions. Furthermore, we report that on the left frontotemporal lobe, the discrimination within the beta band between patients and controls in the word condition was higher than in the nonword condition. The higher discrimination within the word condition can be linked to the abnormalities in language processing in schizophrenia patients. Our results show that the ERD/ERS patterns extracted from MEG can be successfully used in patient-control discrimination with appropriate adjustment of spatial, spectral, temporal and functional process parameters
Optimal waveform selection for radar target classification

We apply a sequential experiment design procedure to the problem of signal selection for radar target classification. Radar waveforms are designed to discriminate between targets possessing a doubly-spread reflectivity function that are observed in clutter. The waveforms minimize decision time by maximizing the discrimination information in the echo signal. Each waveform selected maximizes the Kullback-Leibler (1951) information number that measures the dissimilarity between the observed target and alternative targets. We discuss two scenarios. In the first scenario, the target environment is assumed fixed during illumination. In this case the optimal waveform selection strategy leads to a fixed library of waveforms. During actual classification, the sequence in which the waveforms are selected from the library is determined from the noise to clutter power in the range-Doppler support of the targets. In the second scenario, the target environment changes between pulse transmissions. In this case, the maximum discrimination information is obtained by a repeated transmission of a single waveform designed from the reflectivity function of the targets. We show that our choice of signals can produce significant gains in detection performance.
Eigen-image based video segmentation and indexing

We present a new approach for automatic video scene segmentation and content based indexing. Our approach detects video shots and builds a collection of key frames and representative frames. Scene segmentation and video indexing are based on a temporally windowed principal component analysis of a subsampled version of the video sequence. Two discriminants are derived from the principal components on a frame by frame basis. The discriminants are used for scene change detection and key frame extraction and classification into relevant clips. The system creates an adjacency matrix to build the scene transition graph. The scene transition graph allows easy access to video image-based information.
A performance study of novel Sequential Energy Detection methods for spectrum sensing

We study the sequential energy detection problem in the context of spectrum sensing for cognitive radio networks. We formulate a novel Sequential Energy Detector and provide a comprehensive study of its performance. The sensitivity of the Sequential Test to primary signal variance estimation is addressed for the first time ever in this paper. Specifically, we develop an Iterative Hybrid Bayesian method to robustly estimate the primary signal variance. Through extensive simulations it is demonstrated that our 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 and False Alarm. Our simulations also demonstrate the enhanced robustness gained via the use of the new Variance Estimator which converges in only 10 iterations on average and delivers a performance within 10% of that with perfect knowledge of the actual primary signal variance.
A construction of a space-time code based on number theory

We construct a full data rate space-time (ST) block code over M=2 transmit antennas and T=2 symbol periods, and we prove that it achieves a transmit diversity of 2 over all constellations carved from Z[i]/sup 4/. Further, we optimize the coding gain of the proposed code and then compare it to the Alamouti code. It is shown that the new code outperforms the Alamouti  1998) code at low and high signal-to-noise ratio (SNR) when the number of receive antennas N>1. The performance improvement is further enhanced when N or the size of the constellation increases. We relate the problem of ST diversity gain to algebraic number theory, and the coding gain optimization to the theory of simultaneous Diophantine approximation in the geometry of numbers. We find that the coding gain optimization is equivalent to finding irrational numbers "the furthest," from any simultaneous rational approximations.
Generalized URV subspace tracking LMS algorithm

The convergence rate of the least mean squares (LMS) algorithm is poor whenever the adaptive filter input auto-correlation matrix is ill-conditioned. We propose a new LMS algorithm to alleviate this problem. It uses a data dependent signal transformation. The algorithm tracks the subspaces corresponding to clusters of eigenvalues of the auto-correlation matrix of the input to the adaptive filter, which have the same order of magnitude. The algorithm updates the projection of the tap weights of the adaptive filter onto each subspace using LMS algorithms with different step sizes. The technique also permits adaptation only in those subspaces, which contain strong signal components leading to a lower excess mean squared error (MSE) as compared to traditional algorithms.<<ETX>>
Robust data hiding for images

Data hiding is the process of encoding extra information in an image by making small modifications to its pixels. To be practical, the hidden data must be perceptually invisible yet robust to common signal processing operations. This paper introduces two schemes for hiding data in images. The techniques exploit perceptual masking properties to embed the data in an invisible manner. The first method employs spatial masking and data spreading to hide information by modifying the image coefficients. The second method uses frequency masking to modify the image spectral components. By using perceptual masking, we also increase the robustness of the hidden information. Experimental results of data recovery after applying noise and JPEG coding to the hidden data are included.
Robust movement direction decoders from local field potentials using spatio-temporal qualitative patterns

A major drawback of using Local Field Potentials (LFP) for Brain Computer Interface (BCI) is their inherent instability and non-stationarity. Specifically, even when a well-trained subject performs the same task over a period of time, the neural data observed are unstable. To overcome this problem in decoding movement direction, this paper proposes the use of qualitative information in the form of spatial patterns of inter-channel ranking of multi-channel LFP recordings. The quality of the decoding was further refined by concentrating on the statistical distributions of the top powered channels. Decoding of movement direction was performed using Support Vector Machines (SVM) to construct decoders, instead of the traditional spatial patterns. Our algorithm provides a decoding power of up to 74% on average over a period of two weeks, compared with the state-of-the-art methods in the literature that yield only 33%. Furthermore, it provides 62.5% direction decoding in novel motor environments, compared with 29.5% with conventional methods. Finally, a comparison with the traditional methods and other surveyed literature is presented.
Detection of gradual transitions in video sequences using B-spline interpolation

We present a novel technique for detecting the presence of a gradual transition in video sequences and automatically identifying its type. Our scheme focuses on analyzing the characteristics of the underlying special edit effects and estimates actual transitions by polynomial data interpolation. In particular, a B-spline interpolation curve fitting technique is used. We make use of "goodness" of fitting to determine the presence of gradual transitions. Our approach is able to recover the original transition behavior of an edit effect even if it is distorted by various post-processing stages. Our gradual transition detectors have been extensively tested on various genres of real video sequences to evaluate the performance of the proposed algorithms.
Denoising of multiscale/multiresolution structural feature dictionaries for rapid training of a brain computer interface

Multichannel neural activities such as EEG or ECoG in a brain computer interface can be classified with subset selection algorithms running on large feature dictionaries describing subject specific features in spectral, temporal and spatial domain. While providing high accuracies in classification, the subset selection techniques are associated with long training times due to the large feature set constructed from multichannel neural recordings. In this paper we study a novel denoising technique for reducing the dimensionality of the feature space which decreases the computational complexity of the subset selection step radically without causing any degradation in the final classification accuracy. The denoising procedure was based on the comparison of the energy in a particular time segment and in a given scale/level to the energy of the raw data. By setting denoising threshold a priori the algorithm removes those nodes which fail to capture the energy in the raw data in a given scale. We provide experimental studies towards the classification of motor imagery related multichannel ECoG recordings for a brain computer interface. The denoising procedure was able to reach the same classification accuracy without denoising and a computational complexity around 5 times smaller. We also note that in some cases the denoised procedure performed better classification.
Transcription Factor Discovery using Support Vector Machines and Heterogeneous Data

In this work we analyze the suitability of expression and sequence data for discovery of co-regulatory relationships using Support Vector Machines. In addition, we try to assess the possibility of improving such results by heterogeneous data fusion and by estimating a probability of a correct classification. As shown in other studies, we have found that transcription co-expression is a good estimator for genetic co-regulation. We also have found some evidence that operator site sequence motifs can be used to estimate co-regulation, but the kernels used for feature extraction did not achieve classification rates comparable to expression data. Finally, the additional information provided by combining sequence and expression data can be exploited to estimate the probability of correct classification.
Correlation structure of the discrete wavelet coefficients of fractional Brownian motion

It is shown that the discrete wavelet coefficients of fractional Brownian motion at different scales are correlated and that their auto- and cross-correlation functions decay hyperbolically fast at a rate much faster than that of the autocorrelation of the fractional Brownian motion itself. The rate of decay of the correlation function in the wavelet domain is primarily determined by the number of vanishing moments of the analyzing wavelet.
On the design of vehicle trajectories for high quality multistatic SAR images

We develop guidelines for UAV trajectory design in a multistatic SAR system by studying the singular value behavior of a tomographic measurement matrix, which represents line integral measurements around ellipses in a reflectivity plane. We develop and apply bounding techniques from linear algebra to estimate the performance of the the reconstruction process based on simply-calculated statistics of the measurement matrix. Physical interpretation of the matrix analysis results, in terms of various parameters of the SAR system, is provided. Reconstruction performance for various examples of UAV trajectories are compared to one another and to these bounds.
Capacity of ultra-wideband OFDM

Several researchers have recently shown that the capacity achievable by spread-spectrum DS-CDMA (wideband) signals on multipath fading channels goes to zero as the bandwidth or the number of equal-energy resolvable multipaths (for the case of a channel with a finite number of time-varying paths) increases. In this paper we use a different kind of wideband signaling, based on a hybrid ultra-wideband OFDM technique, proposed by Tewfik and Saberinia. We show that this scheme can achieve capacity on fading channels, as bandwidth goes to infinity provided the signal is scaled in a manner different from the traditional DS-CDMA scheme. In particular, as bandwidth goes to infinity it is better to add more orthogonal frequencies rather than to reduce the signaling pulse duration.
Digital watermarks for audio signals

We present a novel technique for embedding digital "watermarks" into digital audio signals. Watermarking is a technique used to label digital media by hiding copyright or other information into the underlying data. The watermark must be imperceptible or undetectable by the user and should be robust to attacks and other types of distortion. In our method, the watermark is generated by filtering a PN-sequence with a filter that approximates the frequency masking characteristics of the human auditory system. It is then weighted in the time domain to account for temporal masking. We discuss the detection of the watermark and assess the robustness of our watermarking approach to attacks and various signal manipulations.
Multimedia data-embedding and watermarking technologies

We review developments in transparent data embedding and watermarking for audio, image, and video. Data-embedding and watermarking algorithms embed text, binary streams, audio, image, or video in a host audio, image, or video signal. The embedded data are perceptually inaudible or invisible to maintain the quality of the source data. The embedded data can add features to the host multimedia signal, e.g., multilingual soundtracks in a movie, or provide copyright protection. We discuss the reliability of data-embedding procedures and their ability to deliver new services such as viewing a movie in a given rated version from a single multicast stream. We also discuss the issues and problems associated with copy and copyright protection and assess the viability of current watermarking algorithms as a means for protecting copyrighted data.
Multiscale sigma filter and active contour for image segmentation

Image segmentation is essential for image recognition. To achieve image segmentation, it is often desirable to process the image into piecewise smooth regions while preserving or even enhancing important edges. In this paper, we first design a multiscale sigma filter to achieve the above objective. To locate the object more accurately, we then follow the sigma filter processing step and a multiscale active contour method with much simpler implementation than the originally proposed active contour algorithm.
Low complexity customized video playlist generation

We consider the problem of customizing video playlist where a system for constructing a playlist composed of a sequence of videos that attain a specific user profile is proposed. Each video item is represented as a collection of weighted attributes that represent the accompanying features. The proposed solution allows users to construct a sequence of video items by imposing global and transitional constraints. Transitional constraints deal with the similarity between successive video objects while global constraints ensure that the retrieved video attributes attain the proposed user profile. The retrieved sequence of videos is constructed by considering it as a solution to a constrained sequence retrieval problem where constraints represent user preferences. The constrained retrieval problem is then modeled as a network flow model which leads to integer program. Relaxation techniques are then used for complexity reduction. Experiments demonstrate the flexibility of the proposed model in constructing the playlist according to user needs.
Time-scale modification of audio signals with combined harmonic and wavelet representations

We propose a new time-scale modification method for high quality audio signals. Our approach strives to preserve pitch and timbre. In our method, the signal is represented as the sum of sinusoidal components and a residual (edges and noise). The decomposition is computed via a combined harmonic and wavelet representation. Time-scaling is performed on the harmonic components and residual components separately. The harmonic portion is time-scaled by demodulating each harmonic component to DC, interpolating and decimating the DC signal, and remodulating each component back to its original frequency. The residual portion is time-scaled by preserving edges and relative distances between the edges while time-scaling the stationary (noise) components between the edges.
A flexible receiver for CDMA multiuser communications

Code division multiple access (CDMA) capacity is limited by interference amongst users. The effect of this interference on receiver outputs depends on the users' signatures and the actual detector used in the receiver. A matched filter receiver is particularly sensitive to interference, whereas an optimum multiuser receiver is less sensitive but infeasible due to its exponential complexity. We propose a receiver structure that trades detection performance for reduced complexity. It can interpolate between the performances and complexities of these two receivers. Our detector uses a tree structure, and some of its special cases are the decision feedback detector, the decorrelating detector, and the optimal linear detector. We show that at equal complexity levels, a particular implementation of our detector outperforms these detectors. We also show that our approach can be used with a minimum-mean-square-error design criterion and coded CDMA transmission.
Sigma filter based unsupervised color image segmentation

In this paper, we present an unsupervised color image segmentation algorithm. By first processing a color image via the proposed color sigma filter, pixels within the same semantic region become more concentrated around their centroid in the perceptual color coordinate system. A k-mean algorithm is then designed to automatically differentiate the image into non-overlapping semantic objects. Because of the periodicity in the hue component, we apply two manifolds to completely cover the hue vector, and fuse distinguished regions from both manifolds to obtain the final image segmentation. The computational complexity of our algorithm is O(N), where N is the total number of pixels, and no priori information is assumed. We provide examples to illustrate the performance of our procedure.