nils-herrmann/clean_scirepeval huggingface dataset:
Non-cooperative state tracking of a cognitive radio network with multiple primary users via multiple hypothesis testing

This paper proposes an algorithm to enable a secondary user in a cognitive radio network to be aware of whether each primary transmitter is active or passive, namely network state estimation. The number of transmitters is unknown apriori. A secondary user needs to perform a multiple-hypothesis testing, which can be achieved via clustering the received observations such that observations sharing the same cluster are declared to have been generated from the same hypothesis. However, most of the clustering algorithms assume that data is available offline as a batch. We have earlier proposed the LOC algorithm, a Large-scale Online hierarchical Clustering algorithm for unsupervised sequential numerical data. Unlike most of clustering algorithms, hierarchical algorithms do not assume the number of clusters to be known apriori. In this paper, the LOC algorithm is applied in the context of multiple-hypothesis testing to enable each secondary user to track the network state. The received samples at a secondary user are fed to a proposed filter then to the LOC algorithm to obtain a hierarchical tree. We study the choice of an adequate cutting level and evaluate the spectrum sensing performance. We report a 93% average probability of correctly declaring a true hypothesis in a scenario where number of true hypotheses in the network is five.
Signal Processing Trends in Disease Diagnostics Prevention and Management

This talk discusses some trends in diagnostics and treatment anchored in recent advances in signal processing. The talk begins with an introduction to compressed sensing and describes its applications in MRI cardiac imaging. Next, the talk focuses on biclustering techniques and their use in developing group biomarkers for ovarian cancer. The talk concludes with an overview of wireless wearable sensors and their application in the treatment of patients with traumatic brain injuries.
Multigrid algorithm for image reconstruction from Fourier modulus

Two new iterative multigrid algorithms for image reconstruction from Fourier magnitude data are proposed. The first algorithm is based on the error reduction algorithm and the second is based on a quasi-Newton's method. Both algorithms take advantage of a multigrid scheme to speed up the convergence rate of the iterations. Furthermore, they recover more high frequency components of the image than their non-multigrid counterpart. Experimental study has shown the great potential of both algorithms.<<ETX>>
A TDMA based scheduling scheme in 802.11b WLANs with access point

This paper introduces a new scheduling scheme that provides fair access to all stations in 802.11b WLANs. The scheme divides the transmission opportunities between wireless stations according to their operating conditions. Stations that face errors and can't communicate with the access point are given more transmission time at the moment these errors reduce to compensate for their loss of service. In 802.11b wireless stations can operate at different data rates 11, 5.5, 2 and 1 Mbps. From the literature and our simulation we found that operating at lower data rates would degrade the overall performance of the system. Still, it is impractical in developing a schedule scheme to assume that a wireless station can only operate at full data rate as similar previous work. Our scheduling scheme takes in consideration that some stations may operate at data rates lower than 11 Mbps. Simulation results show that fairness between mobile stations is almost achieved.
Genetic variation detection using maximum likelihood estimator

In recent years it has come to be appreciated that submicroscopic DNA copy number differences represent an important source of human genetic variation and contribute significantly to disease susceptibility. Array comparative genomic hybridization has emerged as a powerful tool for assessing copy number change and a number of algorithms have been developed to accurately assign copy number segments while minimizing errors from this inherently variable methodology. In this paper, we present an extended version of our previously proposed algorithm, maximum likelihood estimator, to clearly map and detect copy number variations. The extension accounts for both the unequal spacing distance between the contiguous probes and the regional evaluation of the detected segments based on biological information of the genomic positions. Using genomic DNA from well-characterized cell lines, we compare the performance of the proposed methods. Finally, the experimental results show that our proposed method outperforms other popular commercial programs and published algorithms.
Using Multiresolution Space-Time-Frequency Features for the Classification Motor Imagery Eeg Recordings in a Brain Computer Interface Task

We introduce an adaptive space time frequency analysis to extract and classify subject specific brain oscillations induced by motor imagery in a brain computer interface task. The proposed method requires no prior knowledge of the reactive frequency bands, their temporal behavior or cortical locations. By using a modified local discriminant base algorithm (flexible LDB), our procedure calculates an arbitrary time-frequency segmentation of the given multichannel brain activity recordings to extract subject specific ERD and ERS patterns. The extracted time-frequency features are processed by principal component analysis to reduce the feature set which is highly correlated due to volume conduction and the neighbor cortical regions. The reduced feature set is then fed into a linear discriminant analysis for classification. We describe experimental results for 9 subjects that show the superior performance of the proposed method. The classification accuracy for the subjects varied between 76.4% and 96.8% and the average classification accuracy was 84.9%.
Data hiding for video-in-video

We introduce a scheme for hiding high bit-rate supplementary data, such as secondary video, into a digital video stream by directly modifying the pixels in the video frames. The technique requires no separate channel or bit interleaving to transmit the extra information. The data is invisibly embedded using a perception-based projection and quantization algorithm. The data hiding algorithm supports user-defined levels of accessibility and security. We illustrate our algorithm using examples of real-time video-in-video and speech-in-video. We also demonstrate the robustness of the data hiding procedure to video degradation and distortion, e.g., those that result from additive noise and compression.
Novel pattern detection in children with Autism Spectrum Disorder using Iterative Subspace Identification

Recent increase in the number of Autism cases has triggered an alarm in our society. Lack of effective diagnostics, interventions and associated cost makes early intervention and long term treatment difficult. In this paper, we describe novel methods to assist management by automatically detecting stereotypical behavioral patterns using accelerometer data. We use the Iterative Subspace Identification (ISI) algorithm to learn subspaces in which the sensor data lives. It extracts orthogonal subspaces which are used to generate dictionaries for clustering and for signal representation. It is also applied to detecting segments from acoustics data. We further improve the algorithm by detecting novel events which were not known to the system during the training. Using these methods, we achieved an average of 83% and 90% of classification rates for flapping and rocking behaviors and 93% for novel behavioral patterns studied in this paper.
A Fully Passive Compressive Sensing SAR ADC for Low-Power Wireless Sensors

The compressive sensing (CS) theory states that the sparsity of a signal can be exploited to reduce the analog-to-digital converter (ADC) conversion rate and save power. However, most previous CS frameworks require dedicated analog CS encoders built by power-hungry active amplifiers, which limit the overall power saving. Differently, this paper proposes a fully passive switched-capacitor-based CS framework that directly embeds CS into a successive-approximation-register (SAR) ADC. The proposed CS-SAR ADC can operate in two modes: the Nyquist mode and the CS mode. In the CS mode, the CS-SAR ADC quantizes the input once every four-time sampling, reducing the conversion rate and the circuit power by four times compared to the Nyquist mode. A prototype chip is fabricated in a 0.13-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> CMOS process. At 0.8 V and 1 MS/s, the chip consumes 19.2 <inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula> in the Nyquist mode and <inline-formula> <tex-math notation="LaTeX">$5\mu \text{W}$ </tex-math></inline-formula> in the CS mode. Discrete-tone signals are converted and reconstructed with a peak signal-to-noise plus distortion ratio (SNDR) of 61 dB and maximum bandwidth occupancy of 8.2%. Speech signals are also used to demonstrate the capability of the chip to compressively sense real-world signals. Compared to prior CS works, it improves the post-reconstruction SNDR by 18 dB and the energy efficiency by 13 times.
A feedback based multicasting protocol for efficient video on demand

Video on demand services require video multicasting protocols to provide efficient and reliable performances over various client request rates. While reactive protocols perform very well when video demand is low, well-known reactive protocols can not effectively handle these videos in situations of high demand. In this work we have developed an efficient video multicasting protocol, the feedback based multicasting protocol, that requires lower bandwidth to multicast a video at any access rate. The proposed protocol uses an online stream merging scheme, requiring lower merging costs than other previous schemes. We have provided both analysis and simulation to show the performance gain over previous protocols. From an analytical method, we found the expected service bandwidth of the feedback based multi-casting protocol (FMP) is less than that of previous reactive protocols. Furthermore, The FMP shows similar bandwidth requirement to one of the best existing proactive protocols at high client request rates.
Improving the security of watermark public detectors

In this paper, we analyze the watermark detection when the decoder is publicly available. This situation puts new challenges of current watermark design to assure the decoder security, The common feature among most watermarking algorithms is that the decision boundary can be completely specified by a finite set of parameters. Hence, it can be estimated using a finite number of samples. In this paper, we describe a generalized attack for removing the watermark with minor perceptual distortion and establish its effectiveness using a direct implementation with least mean square (LMS) algorithm. The attack is abstract and can be applied to image, audio, and video watermarking schemes. Next, we describe a counterattack by using fractal decision boundaries, which are obtained by processing the original boundary. The resulting boundary cannot be parameterized and hence it cannot be estimated using a finite number of samples. The performance of the new technique is essentially similar to any watermarking algorithm from which it is derived.
Coding of ECG signals by wavelet transform extrema

A new electrocardiogram (ECG) coding method is presented. The method is based on storing the extrema of the wavelet transform of the ECG signal. The finite wavelet packet decomposition of the sampled ECG signal is obtained using the perfect reconstruction filterbank of the transform. The extrema of the resulting high-frequency subsignals are detected and the ones representing the clinically significant peak information are stored. The low-resolution subsignal is compressed by a PCM coder.<<ETX>>
A novel update propagation module for the data provenance problem: a contemplating vision on realizing data provenance from models to storage

To date, the systems approach to science, which emphasizes the connections among phenomena studied at different scales and by different disciplines, is causing dramatic changes in how scientific results are communicated. These changes drive a shift on how to propagate data with certain properties so that it can be used intelligently by others. In this work we elaborate on three major factors governing the propagation module of data provenance. The proposed propagation module provides an efficient solution for many critical problems in the management and provenance of scientific data. Unlike previous work, our work aims at realizing data provenance from models to storage. The natural representation of data as objects and its utility for capturing provenance has led us to consider a new storage architecture based on the object-based storage (OSD) technology. An outline of this framework is discussed.
Audio coding using steady state harmonics and residuals

In this paper, we solve the problem of inter and intra-frame frequency leakage in harmonic based audio coding approaches. Specifically, we first use an FFT to estimate the harmonic parameters. Because of the limited frequency resolution of the windowed FFT, there is an uncertainty in the measured frequency within one frequency bin. To minimize this uncertainty, we use these estimates as seeds to a minimum least squares problem and solve for the optimal harmonic parameters. The resulting residual should be essentially free of harmonics and consist of only attacks and noise. We are preserving the attacks and coding them separately. The resulting residual noise is modeled and coded using wavelet transforms and noise modeling. We also present a solution to the problem of inter-frame frequency fading and highlight the benefit of using a rectangular coordinate representation of the harmonic data. The improved coder that we propose can be viewed as an object based approach to audio coding and leads to perceptually pleasant progressive transmission and representation schemes for multimedia applications.
Adaptive compressed sensing for sparse signals in noise

This paper studies the problem of recovering a signal with a sparse representation in a given orthonormal basis using as few noisy observations as possible. Herein, observations are subject to the type of background clutter noise encountered in radar applications. Given this model, this paper proves for the first time that highly sparse signals contaminated with Gaussian background noise can be recovered by adaptive methods using fewer noisy linear measurements than required by any possible recovery method based on non-adaptive Gaussian measurement ensembles.
Extraction of Optimal Time-Frequency Plane Features for Classification

A method based on local discriminant bases is developed to extract discriminating features for classification from time-frequency pattern of one dimensional signals. Acoustic signals from two classes are first divided into segments along the time axis according to their discrimination power. The signals in time segments are then decomposed into subbands in binary tree structure by using undecimated wavelet transform. The subband tree is then pruned by assessing the discrimination power of the nodes. The resulting time-frequency map indicates the location of the best features for classification. This map is then used to extract features to be used for classification. It is observed that the extracted features increase the classification accuracy compared to various features previously used for the same problem.
Compact angular support beams for space-invariant vector flow mapping

The authors define a special class of compact angular spectrum beams (CASBs) that can be used to provide space-invariant estimation of the velocity vector from a single coplanar aperture. By space-invariance the authors mean both range and azimuth flow shift-invariance of velocity estimation. They analytically show that some of the non-diffractive beams, e.g., Bessel beams, are sub-classes of CASBs. They show that CASBs have infinite spatial support and thus, they cannot be generated in practice. They discuss finite support approximation of such beams. These approximations can be implemented using currently available annular and two-dimensional transducer arrays. The authors also describe the related class of spatially modulated CASBs. These beams provide range-invariant true-velocity estimation from frequency shift measurements.
A Subspace Approach to Learning Recurrent Features From Brain Activity

This paper introduces a novel technique to address the instability and time variability challenges associated with brain activity recorded on different days. A critical challenge when working with brain signal activity is the variability in their characteristics when the signals are collected in different sessions separated by a day or more. Such variability is due to the acute and chronic responses of the brain tissue after implantation, variations as the subject learns to optimize performance, physiological changes in a subject due to prior activity or rest periods and environmental conditions. We propose a novel approach to tackle signal variability by focusing on learning subspaces which are recurrent over time. Furthermore, we illustrate how we can use projections on those subspaces to improve classification for an application such as brain-machine interface (BMI). In this paper, we illustrate the merits of finding recurrent subspaces in the context of movement direction decoding using local field potential (LFP). We introduce two methods for using the learned subspaces in movement direction decoding and show a decoding power improvement from 76% to 88% for a particularly unstable subject and consistent decoding across subjects.
Enhanced localization in wireless personal area networks

In this paper, we consider the problem of providing highly accurate time delay or ranging estimates using multiple receptions of ranging signals in multi-band communication systems. We describe a novel ranging approach that reduces the variance of the time delay estimate by M/sup 3/ in white Gaussian noise. The new technique synthesizes the return corresponding to a virtual large bandwidth signal by appropriately merging the returns of M low bandwidth ranging signals from M different sub-bands. These low bandwidth signals can be generated by decomposing a high time resolution large bandwidth signal using a full-tree wavelet decomposition filter bank. The corresponding perfect reconstruction synthesis filter bank is used at the receiver to combine the received signals. Alternatively, we show that the procedure can rely on the preamble sequences used in the proposed IEEE 802.15.3a wireless PAN standard and other communication systems. We study the effect of fading and multipath channels on the performance of the proposed scheme. Simulation results are provided to compare the performance of this scheme with traditional ones.
Effect of beampattern on directional beacon based position location algorithm for UWB systems

Motivated from the results of previous studies, we aim to evaluate the effect of antenna beampattern on the performance of directional beacon based position location algorithm in this paper. We make use of earliest arrivals to detect the line of sight components. Directional beacons are generated using four element uniform linear antenna array (ULA) at the transmitting nodes. To achieve narrow beamwidth, we design the beampattern using Dolph-Chebyshev method that provides a way to tradeoff beamwidth for sidelobes level in an optimum manner. Simulation results show that the minimum error in time estimation (consequently position location) does not occur at the minimum beamwidth. The increase in range estimation error at the minimum beamwidth is due to the significant sidelobes level at this point. Through simulation results, we show that the minimum error in range estimation occurs at 0.33 rad beamwidth for 20 dB SNR case.
Combined audio and visual streams analysis for video sequence segmentation

We present a new approach to video sequence segmentation into individual shots. Unlike previous approaches, our technique segments the video sequence by combining two streams of information extracted from the visual track with audio track segmentation information. The visual streams of information are computed from the coarse data in a 3-D wavelet decomposition of the video track. They consist of (i) information derived from temporal edges detected along the time evolution of the intensity of each pixel in temporally sub-sampled spatially filtered coarse frames, and (ii) information derived from the coarse spatio-temporal evolution of intra-frame edges in the spatially filtered coarse frames. Our approach is particularly matched to progressively transmitted video.
Fast dynamic magnetic resonance imaging using tagging RF pulses

A critical requirement for dynamic magnetic resonance imaging (MRI) is to reduce image acquisition times while maintaining high spatial resolutions to capture the underlying process with high-information rates. This paper presents a sparse signal recovery based fast MRI method which uses: 1) dictionary learning for sparse representation of signals for encoding of data redundancy in physiological functions and, 2) a tagging radio-frequency pulses based novel MR signal encoding formulation to uniformly sample the k-space, even at high acceleration factors. The preliminary results of dynamic MR image recovery experiments using tagging based MR signal acquisition method on an in-vivo myocardial perfusion dataset outperforms the equivalent dynamic MRI method implemented with variable density k-space under-sampling.
Adaptive Strategies for Target Detection and Localization in Noisy Environments

This paper studies the problem of recovering a signal with a sparse representation in a given orthonormal basis using as few noisy observations as possible. Herein, observations are subject to the type of background clutter noise encountered in radar applications. Given this model, this paper proves for the first time that highly sparse signals contaminated with Gaussian background noise can be recovered by adaptive methods using fewer noisy linear measurements than required by any possible recovery method based on nonadaptive Gaussian measurement ensembles.
A binary wavelet decomposition of binary images

We construct a theory of binary wavelet decompositions of finite binary images. The new binary wavelet transform uses simple module-2 operations. It shares many of the important characteristics of the real wavelet transform. In particular, it yields an output similar to the thresholded output of a real wavelet transform operating on the underlying binary image. We begin by introducing a new binary field transform to use as an alternative to the discrete Fourier transform over GF(2). The corresponding concept of sequence spectra over GF(2) is defined. Using this transform, a theory of binary wavelets is developed in terms of two-band perfect reconstruction filter banks in GF(2). By generalizing the corresponding real field constraints of bandwidth, vanishing moments, and spectral content in the filters, we construct a perfect reconstruction wavelet decomposition. We also demonstrate the potential use of the binary wavelet decomposition in lossless image coding.