nils-herrmann/clean_scirepeval huggingface dataset:
Hyperexponential approximation of channel idle time distribution with implication to secondary transmission strategy

Channel idle time distribution (CITD) based secondary transmission strategies have been studied intensively in the literature. The performance of secondary devices are limited by the presumed CITD. But none of them provides a reasonable characterization of the CITD in reality. In this paper, we carefully examine the feature of the CITD using large amount of channel idle time data collected in various WLAN networks. It is demonstrated that the CITD can be well approximated by hyperexponential densities. Based on the estimated density, we propose a novel multiple-shot transmission strategy which takes advantage of the hyperexponential feature of the CITD. This strategy achieves at least twice the increase for the data we examine in average secondary access time compared to conventional transmission strategies.
Efficient decoding of watermarking schemes in the presence of false alarms

We treat the problem of recovering the correct message if extra bits (false alarms) are added to the body of the message at random locations. This situation is common for some watermarking systems with selective embedding which is typical when the human visual (or audio) system is incorporated to enhance the quality of the watermarking system. We propose an efficient decoding scheme for convolutional codes using some modifications of the Viterbi algorithm. Extra states are added to represent the possible false alarms and a new expansion algorithm of the modified trellis diagram is proposed. The simulation results show the high efficiency of our algorithm in detecting random false alarms with high rates.
An efficient maximum entropy technique for 2-D isotropic random fields

A linear MEM (maximum-entropy spectral estimation method) algorithm for 2-D isotropic random fields is introduced. Unlike general 2-D covariances, isotropic covariance functions that are positive definite on a disk are known to be extendible. A computationally efficient procedure is developed for computing the MEM isotropic covariance function that is given over a finite disk of radius 2R. It is shown that the isotropic MEM problem has a linear solution and that it is equivalent to the problem of constructing the optimal linear filter for estimating the underlying isotropic field at a point on the boundary of a disk radius R, given noisy measurements of the field inside the disk. The procedure is guaranteed to yield a valid isotropic spectral estimate and is computationally efficient since it requires only O(BRL/sup 2/) operations, where L is the number of points used to discretize the interval (0, R) and B is the bandwidth in the wave-number plane of the spectrum that to be estimated. Examples are presented to illustrate the behaviour of the algorithm and its high-resolution property.
Blind source separation using monochannel overcomplete dictionaries

We propose a new approach to underdetermined blind source separation (BSS) using sparse decomposition over monochannel dictionary atoms and compare it to multichannel dictionary approaches. We show that the new approach is easily extended to any single channel decomposition method and allows for faster computation of algorithms such as the bounded error subset selection (BESS) because of the reduced dimension of the search space. Experimental results on matching pursuit (MP) and BESS algorithms show that our method can give better signal to interference ratio performance than pursuit methods based on multichannel dictionary atoms.
Image coding with wavelet representations, edge information and visual masking

The wavelet transform provides a multiresolution representation of images. Edges, which are visually important, produce large coefficients across several scales in the wavelet transform domain. By tracking and predicting these edge coefficients across scales in the wavelet transform domain, we can greatly improve the compressed image quality with little degradation in compression ratio. This paper proposes a novel model-based edge tracking and prediction in the wavelet domain. It separates textures from edges and codes them differently. Edges are coded via an edge tracking and prediction, while textures are coded with either ordinary wavelet based image coding techniques or a "wavelet-like" filter bank which is similar to the tuning channels in the human vision system. The coding noise is then coded with a noise modelling. Visual masking models are also used to ensure the compressed image has little or almost no perceptual distortion.
Using feedback in long term trajectory decoding from Local Field Potentials

In this paper, we study a feedback mechanism in the design of a Local Field Potential (LFP) based Brain Computer Interface (BCI) that decodes arm movements. A major setback of using Local Field Potentials based BCI is their non-stationarity. In addition, many proposed BCI devices are usually trained and simulated in an open-loop environment, neglecting the effect of user adaptation in the loop. To tackle these problems, a Hammerstein-Wiener based decoder is proposed to model the nonlinearities of the system in a modular fashion. Furthermore, partial feedback is incorporated to achieve high decoding performance, particularly when decoding data from trials conducted as many as 14 days following initial BCI training.
Probabilistic modeling of multi-level genetic regulatory logic

We propose a new class of models for genetic regulatory interactions that extends prior work by Bulashevska and Eils. Our model uses a probabilistic multi-level logic semantic framework to account for noisy biological processes and observed data. In particular, the expression levels of genes take more than two values and the evolution of the genetic regulatory network follows a hidden Markov model. We illustrate our model building procedure using an 'OR' function.
Designing interventions to mitigate cognitive biases in human decisions

Human decision-making is adversely affected by cognitive biases. In this paper we construct an example that incorporates an anchor-and-adjustment model for belief-updating when information is presented sequentially to a human. We prove that different orderings of the same information pieces impact the human decision-making by deriving closed-form expressions of the corresponding error probabilities. We then backpropagate the error for one selected ordering of the information and present two strategies to reduce the order-effects and improve the decision-making in presence of cognitive biases: either modify the information presented to the human taking into account history and context or teach the human to modify the decision boundaries for the given ordering of the observations. The methods generalize to more general settings.
True velocity estimation using the correlation technique [blood flow]

One of the most important biomedical applications of ultrasound is its use in the detection, measurement and mapping of blood flow. The correlation technique has been shown to give a better accuracy velocity estimation than the conventional frequency domain methods. This technique requires two consecutive echo signals in addition to the flow angle to produce the magnitude of the velocity vector based on transit-time calculations. The effect of the lateral beam pattern on the results has not been sufficiently treated in the literature. In this work, we present a new generalized formulation of the correlation technique that incorporates both axial and lateral beam characteristics. We show that the location of the correlation function peak given in the literature is a special case of this model when the field is a plane wave. Also, we show that for other practical beam forms, the lateral beam form introduces an angle-dependent bias to the axial velocity measurements obtained with the classical formula. By properly choosing the source aperture and excitation signal, we derive a formula for lateral displacement estimation from correlation peak locations and magnitudes. This displacement can be used directly to estimate the lateral velocity component and allow the calculation of the complete velocity vector.
Harmonic retrieval in the presence of colored noise

A novel procedure for estimating the number and frequencies of complex exponential signals in the presence of an unknown colored background noise is developed. Under the assumption that the background noise has a rational power spectrum (or a power spectrum that can be closely approximated by a rational one), it is shown that the number and frequencies of the complex exponentials can be estimated by rooting a polynomial that is formed from elements at the vectors spanning the null-space of a Hankel matrix. The entries of this matrix are formed from the correlation sequence of the noisy observations corresponding to the complex exponentials. A method for separating the roots of the polynomial that are due to the complex expotentials from those that are due to the noise is also presented. It is based on properties of the Szego polynomials associated with the correlation sequence of the noisy observations corresponding to the complex exponentials.<<ETX>>
Computational analysis of functionally related genes in mouse response to Plasmodium chabaudi

According to the World Health Organization (WHO), over 1-3 million children under the age of five die due to malaria each year. The aim of this project is to increase our understanding of the Plasmodium chabaudi parasite and of its interactions with the mammalian host using a computational approach. This is achieved by applying a time-series clustering algorithm to a publicly available short time-series gene expression data representing two disease states (P. chabaudi infected and non infected), two genders (male and female), two protocols (intact and gonadectomized), and four time points (0, 3, 7, and 14 days after inoculation) of Mus musculus (mouse) response to P. chabaudi infection. Results obtained provide a rigorous statistical explanation to the fact that intact males were more likely than intact females to die following P. chabaudi infection on one hand, and on the other hand, gonadectomy of male and female mice altered these sex-associated differences. These findings may suggest that sex steroid hormone modulates immune responses to pathogen attacks.
Visual masking and the design of magnetic resonance image acquisition

This paper presents novel data acquisition schemes for improving the quality of magnetic resonance images (MRI). These approaches determine the number of times that k-space (frequency domain) samples have to be acquired and averaged to form an MRI image, by taking into account the characteristics of the human visual system and the location in k-space of the data samples. They lead to dramatic reductions in imaging time (by a factor of 8 to 10) with no reduction in image quality. Alternatively, they produce higher quality imagery using the same amount of imaging time as conventional approaches.
Framework for the analysis of genetic variations across multiple DNA copy number samples

Genetic diseases are characterized by the presence of genetic variations. These variations can be described in the form of copy number. Microarray-based comparative genomic hybridization is a high-resolution technique used to measure copy number variations. However, the observed copy numbers are corrupted by noise, making variations breakpoints hard to detect. In this paper, we provide a framework for the analysis of copy number. The first part of the framework uses an extended version of nonlinear diffusion filter as pre-processing technique to denoise the observed data base. The extension accounts for the nonuniform physical distance between probes. The second part uses estimates the relative frequency of local and global genomic variations across multiple samples to identify statistically and biologically significant variations. For evaluation, we provide copy number variations results using simulated and real data samples. We also validate the predicted copy number variation segments of copy number gain and copy number loss using the experimental molecular tests quantitative polymerase chain reaction and show that our proposed approach is superior to popular commercial software.
An Overview of Signal Processing for Food Inspection [Applications Corner]

The goal of this article is to introduce the signal processing community to the challenges that arise in food inspection. We briefly describe both traditional food-inspection technologies, which rely on sample collection and subsequent offline analysis in a laboratory, and newer approaches that use nondestructive methods to measure various quality parameters of food products in real time. We focus on four specific examples to illustrate the breadth of technologies currently in use in food inspection and the challenges that remain to be addressed. In each case, we describe the problem setting and its economic and health aspects; the techniques that are used, including the physical principles on which these techniques are based; and their performance and cost
Optimal waveform selection in range-Doppler imaging

Solves the problem of adaptively selecting a set of N radar waveforms to obtain the optimal L/sub 2/ estimate of a target range-Doppler reflectivity function. The authors assume that the targets correspond to one of two classes. They show that the waveform selection strategy involves an initial class identification phase followed by a pure imaging phase. They provide a recipe for minimizing the number of waveforms needed for the class identification phase. They show that this leads to an optimal adaptive target imaging strategy.<<ETX>>
Convolutional decoding for channels with false alarms

In this work, we propose a new channel model that is suited for systems with irregular sampling, e.g. selective data embedding in digital media. The channel model accounts for the possible occurrence of false alarms, i.e. extra data bits, in the received sequence. We propose modifications to the common decoding schemes of the convolutional codes namely, the Viterbi and the sequential decoding to compensate for these false alarms. The simulation results establish the effectiveness of the proposed algorithms in detecting false alarms with high rates.
Infostation information transfer using hybrid ARQ technique combined with adaptive modulation

In this paper we analysis the performance of Infostation information transfer using a hybrid automatic repeat request (ARQ) technique combined with adaptive modulation (AM). The ARQ scheme works in selective-repeat (SR) mode combined with adaptive modulation and maximum ration combining (MRC). First, we explain how our hybrid ARQ/AM scheme works to transmit fragmented information files. Then, we present numerical evaluation of average number of channel access (ACHA) needed to complete a file transfer.
An efficient maximum entropy technique for 2-D isotropic random fields

A novel linear maximum-entropy method (MEM) spectral-estimation algorithm for 2-D isotropic random fields is presented. This procedure differs from pervious 2-D MEM algorithms by the fact that maximum advantage is taken of the symmetries implied by isotropy. It is shown that the isotropic MEM problem has a linear solution and that it is equivalent to the problem of constructing the optimal linear filter for estimating the underlying isotropic field at a point on the boundary of a disk of radius R, given noisy measurements of the field inside the disk. A fast algorithm for computing the estimation filter is then used to obtain the MEM spectral estimate.<<ETX>>
Hybrid wavelet transform filter for image recovery

Wavelet denoising techniques are used to remove additive Gaussian noise by thresholding the wavelet coefficients. Like other transform based filters, wavelet shrinkage methods provide blur or visual artifacts that are exhibited in the neighborhood of image edges. The motive for implementing the hybrid wavelet transform filter (HWTF) is to provide a discriminate smoothing operator for noise removal. The undesirable smoothing effects can be minimized by performing two stages of wavelet de-noising and gray scale transform methods. The experimental results show that the proposed nonlinear wavelet filtering for noise reduction is an efficient technique to improve image quality.
Sequential pilot sensing of ATSC signals in IEEE 802.22 cognitive radio networks

The IEEE 802.22 Standard promises to be the first practical implementation of the cognitive radio technology. It is based on dynamic spectrum sensing and opportunistic access of the bands that are not currently in use by TV transmitters. FFT based pilot sensing of the TV signal is recommended in the standard. The sensing is carried out by averaging over a fixed number of multiple dwells (6-10). We propose a sequential version of the scheme where the number of dwells required is dynamically varying according to the fidelity of the received signal. We show via simulation that the proposed sequential sensing strategy yields a throughput gain and reduces system overhead.
Speaker identification and video analysis for hierarchical video shot classification

We present a new video shot classification and clustering technique to support content-based indexing, browsing and retrieval in video databases. The proposed method is based on the analysis of both the audio and visual data tracks. The visual stream is analyzed using a 3-D wavelet transform and segmented into shot units which are matched and clustered by visual content. Simultaneously, speaker changes are detected by tracking voiced phonemes in the audio signal. The clues obtained from the video and speech data are combined to classify and group the isolated video shots. This integrated approach also allows effective indexing of the audio-visual objects in multimedia databases.
A Framework for Inference Using Goodness of Fit Tests Based on Ensemble of Phi-Divergences

In this paper we study the inferential use of goodness of fit tests in a non-parametric setting. The utility of such tests will be demonstrated for the test case of spectrum sensing applications in cognitive radios. We provide the first comprehensive framework for decision fusion of an ensemble of goodness-of-fit testing procedures through an Ensemble Goodness-of-Fit test. Also, we introduce a generalized family of functionals and kernels called Φ-divergences which allow us to formulate goodness-of-fit tests that are parameterized by a single parameter. The performance of these tests is simulated under Gaussian and non-Gaussian noise in a MIMO setting. We show that under uncertainty in the noise statistics or non-Gaussianity in the noise, the performance of non-parametric tests in general, and phi-divergence based goodness-of-fit tests in particular, is significantly superior to that of the energy detector with reduced implementation complexity. In particular, the false alarm rates of our proposed tests is maintained at a fixed level over a wide variation in the channel noise distributions. Additionally, we describe a collaborative spatially separated version of the test for robust combining of tests in a distributed spectrum sensing setting and quantify the significant collaboration gains achieved.
Robust long term neural signal decoding by estimating unobserved features

Chronic effects of electrode implantation in the brain tissue alter the neural channel signal-to-noise ratio (SNR) over time. Variability of signal quality over time poses a difficult challenge in long-term decoding of neural signals for Brain Computer Interface (BCI). Specifically, all channels observed during a neural recording session may not be observed during the next recording session. This paper describes a novel approach that effectively overcomes these challenges by identifying reliable channels and features in any given trial, estimating unobservable or unreliable features and adapting the neural signal classifier with no user input in real time. The proposed decoder predicts one of eight arm directions with an accuracy, unmatched in the literature, of above 90% in two monkeys over 4-6 weeks, achieving robustness against time and also varying environmental conditions. Application of these decoders reduces neural prosthetic training time and user frustration thus improving the usability of BCI.
Overcoming measurement time variability in brain machine interface

We introduce a subspace learning approach for multi-channel Local Field Potentials (LFP), and demonstrate its application in movement direction decoding for 8 directions movement. We show that the subspace learning method can effectively address the issue of signal instability across recording sessions by extracting recurrent features from the data. We present results for movement direction decoding, where we trained on two recording sessions, and evaluated decoding performance on a third session. We combine our method with a classifier based on Error-Correcting Output Codes (ECOC) and Common Spatial Patterns (CSP) and found improvement in Decoding Power (DP) from 76% to 88% for a subject known to have strong inter-session variability. Furthermore, we saw an increase from 86% to 90% DP with another subject which exhibited significantly less variability.
Classification of closed and open shell pistachio nuts using principal component analysis of impact acoustics

An algorithm was developed to separate pistachio nuts with closed shells from those with open shells. It was observed that upon impact on a steel plate, nuts with closed shells emit different sounds than nuts with open shells. Two feature vectors extracted from the sound signals were Mel cepstrum coefficients and eigenvalues obtained from the principle component analysis of the autocorrelation matrix of the signals. Classification of a sound signal was done by linearly combining feature vectors from both Mel cepstrum and PCA feature vectors. An important property of the algorithm is that it is easily trainable. During the training phase, sounds of the nuts with closed shells and open shells were used to obtain a representative vector of each class. The accuracy of closed shell nuts was more than 99% on the test set.
Parallel Biclustering of Genes with Coherent Evolutions: Algorithm and Biological Significance of the Biclusters

Uncovering genetic pathways is equivalent to finding clusters of genes with expression levels that evolve coherently under subsets of conditions. 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 non-decreasing 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. We also discuss the biological significance of the biclusters computed by the algorithm for a set of yeast gene microarray data