nils-herrmann/clean_scirepeval huggingface dataset:
Sparse common spatial patterns in brain computer interface applications

The Common Spatial Pattern (CSP) method is a powerful technique for feature extraction from multichannel neural activity and widely used in brain computer interface (BCI) applications. By linearly combining signals from all channels, it maximizes variance for one condition while minimizing for the other. However, the method overfits the data in presence of dense recordings and limited amount of training data. To overcome this problem we construct a sparse CSP (sCSP) method such that only subset of channels contributes to feature extraction. The sparsity is achieved by a greedy search based generalized eigenvalue decomposition approach with low computational complexity. Our contributions in this study are extension of the greedy search based solution to have multiple sparse filters and its application in a BCI problem. We show that sCSP outperforms traditional CSP in the classification challenge of the multichannel ECoG data set of BCI competition 2005. Furthermore, it achieves nearly similar performance as infeasible exhaustive search and better than that of obtained by L1 norm based sparse solution.
Ranging in Multiband Ultrawideband Communication Systems

In this paper, we consider the problem of providing highly accurate time-delay or ranging estimates using multiple receptions of ranging signals in multiband communication systems. Specifically, we propose an algorithm to enhance the resolution of range and time-delay estimation in a multiband orthogonal frequency-division multiplexing system that has been proposed for the IEEE 802.15.3a wireless personal area networks. While this paper does not specifically address the problem of accurate localization in multipath nonline-of-sight environments, the proposed approach enhances the resolution and can be combined with any of the known techniques designed for such environments to provide an enhanced localization performance. Prior research has shown that the variance of the time-delay estimation error in white Gaussian noise is inversely proportional to the energy of probe signal and the square of its mean square bandwidth. In a multiband system with M subbands, traditional ranging techniques reduce the variance of the delay estimation from a single subband signal by a factor of M by properly combining M probing signals from all subbands. In contrast, we describe a new approach that synthesizes the return corresponding to a virtual large-bandwidth signal by appropriately merging the returns of M low-bandwidth ranging signals at the receiver. We show that using this technique, the variance of the delay estimation is reduced by a factor of M3. We also show that the proposed scheme achieves the Cramer-Rao lower bound under the high signal-to-noise ratio condition.
Wavelets in optimal radar range-Doppler imaging

It is shown that the most accurate reconstruction of a range-Doppler target density that can be computed from N waveforms and their echoes is obtained by transmitting the singular functions corresponding to the N largest singular values of two kernels derived from the target density. The singular functions are valid wavelets that obey an additional orthogonality constraint in the frequency domain. Using this result, the author briefly discusses a solution to the problem of choosing a set of N waveforms to reconstruct with high accuracy an arbitrary unknown target range-Doppler density function.<<ETX>>
Signal modeling with filtered discrete fractional noise processes

Filtered versions of fractionally differenced Gaussian noise (fdGn) processes are studied. Fractionally differenced Gaussian noise is a discrete-time equivalent of fractional Brownian motion. Filtered versions of such processes are ideally suited for modeling signals with different short-term and long-term correlation structure. Two iterative algorithms for estimating the parameters of filtered fdGn processes are described. The first technique is based on the expectation-maximization algorithm. It converges to a stationary point of the log-likelihood function corresponding to the parameters of the model. The second technique is a computationally efficient approximate approach. It is found to converge experimentally, but no proof of its convergence is given. The usefulness of filtered fdGn models and the performance of the proposed iterative algorithms are illustrated by fitting filtered fdGn models to speech waveforms and other data corresponding to natural phenomena.
Image watermarking by moment invariants

We present a novel technique for watermarking digital images based on its moments. The watermark is composed of the mean of several functions of the second and third order moments designed to be invariant to scaling and orthogonal transformations this has the advantage of making the watermark image dependent. The watermarked image is a linear combination of the original image and a weighted nonlinear transformation of the original. The weight is computed such that the mean of the watermarked image invariants is a predefined number. Watermark detection is as simple as computing the moment invariants of the received image. Our approach has a guaranteed visual transparency up to a contrast modification. The proposed algorithm has proved to be highly robust to all geometric manipulations, filtering, compression and small cropping which are performed as part of StirMark attacks as well as noise addition, both Gaussian and salt and pepper.
Completeness of arbitrarily sampled discrete time wavelet transforms

An arbitrarily sampled discrete time wavelet transform is said to be complete if it is uniquely invertible, i.e., if the underlying signal can be uniquely recovered from the available samples of the wavelet transform. We develop easy-to-compute necessary and sufficient conditions and necessary but not sufficient conditions for the completeness of an arbitrarily sampled dyadic discrete time wavelet transform of a periodic signal. In particular, we provide necessary and sufficient conditions for completeness of the sampled wavelet transform when the lowpass filter associated with the dyadic wavelet filter bank has no unit circle zeros other than those at z=1. We present necessary but not sufficient conditions for completeness when the lowpass filter associated with the dyadic wavelet filter bank has arbitrary unit circle zeros. We also provide necessary and sufficient conditions for completeness of a set of samples of both the lowpass approximation to the signal and its wavelet transform. All the conditions we derive use only information about the location of the retained samples and the analyzing wavelet filter bank. They can easily be checked without explicitly computing of the rank of a matrix. Finally, we present a simple signal reconstruction procedure that can be used once we have determined the arbitrarily sampled discrete time wavelet transform is complete.
Segregation-based subspace clustering for huge dimensional data

Clustering algorithms break down when the data points fall in huge-dimensional spaces. To tackle this problem, many subspace clustering methods were proposed to build up a subspace where data points cluster efficiently. The bottom-up approach is used widely to select a set of candidate features, and then to use a portion of this set to build up the hidden subspace step by step. The complexity depends exponentially or cubically on the number of the selected features. In this paper, we present SEGCLU, a SEGregation-based subspace CLUstering method which significantly reduces the size of the candidate features' set and has a cubic complexity. This algorithm was applied at noise-free data of DNA copy numbers of two groups of autistic and typically developing children to extract a potential bio-marker for autism. 85% of the individuals were classified correctly in a 13-dimensional subspace.
On the optimal choice of a wavelet for signal representation

Two techniques for finding the discrete orthogonal wavelet of support less than or equal to some given integer that leads to the best approximation to a given finite support signal up to a desired scale are presented. The techniques are based on optimizing certain cost functions. The first technique consists of minimizing an upper bound that is derived on the L/sub 2/ norm of error in approximating the signal up to the desired scale. It is shown that a solution to the problem of minimizing that bound does exist and it is explained how the constrained minimization over the parameters that define discrete finite support orthogonal wavelets can be turned into an unconstrained one. The second technique is based on maximizing an approximation to the norm of the projection of the signal on the space spanned by translates and dilates of the analyzing discrete orthogonal wavelet up to the desired scale. Both techniques can be implemented much faster than the optimization of the L/sub 2/ norm of either the approximation to the given signal up to the desired scale or that of the error in that approximation.
Geometric distortion correction through image normalization

A new approach for geometric distortion correction based on image normalization is presented in this paper. By normalization we mean geometrically transforming the image into a standard form. The parameters by which the image is normalized are estimated from the geometric moments of the image. This paper presents a system in which the watermark is embedded and detected in the normalized image. The watermark can then be embedded and detected in the normalized image regardless of its size, orientation and flipping direction.
Performance of N-tone sigma-delta modulators for UWB-OFDM

Due to the spectrum gap between subcarriers in an UWB-OFDM system, an N-tone sigma-delta modulator can be used to introduce appropriately placed nulls into the noise spectrum of such systems. The resulting performance is evaluated in terms of in-band quantization noise and excess resolution gained. Moreover, we relate the performance of our system to that of a traditional oversampled sigma-delta modulator operating in a lowpass system. A higher-order N-tone sigma-delta modulator having better noise shaping ability is also introduced for use in UWB-OFDM systems.
Turbo decoding for generalized channels

We propose a turbo decoding framework in the presence of insertions and deletions as well as substitutions in the received sequence. The decoding algorithm is based on the Viterbi algorithm for this special channel. The synchronization information is exchanged between decoders in the proposed structure and extrinsic information is used, as is commonly done in turbo decoders. Simulation results indicate that the proposed turbo structure does indeed improve the performance of the Viterbi algorithm, particularly at high code rates.
A novel parallelized goodness-of-Fit test based Dynamic Spectrum access technique for Cognitive Radio Networks

Cognitive Radio Networks (CRNs) operate on the principle of opportunistically exploiting unused capacity in the primary network via Dynamic Spectrum Sensing. In this paper we propose a novel suite of transmit opportunity detection methods that effectively exploit the white/gray space in high traffic packet networks. We present a radically different paradigm to exploit the excess Signal-to-Noise ratio regime in which the primary network usually operates via a modification of the Interference Temperature concept. The proposed method is based on robust and rapid detection of changes in the primary network statistics through the use of a novel Parallelized Goodness-of- Fit test. The instantaneous transmit margin afforded by the primary network is dynamically determined and the CRN backs off whenever it detects that it is beginning to interfere with the primary network. We have implemented the proposed method on a CRN testbed that coexists with a large scale IEEE 802.11 primary network and demonstrate its excellent performance through extensive real world experimental results. We show that we can obtain more than 95 % probability of detection of interference while the probability of not detecting a valid transmit opportunity is less than 20 % for detection times of 400–1000 ms.
Multi-user UWB-OFDM communications

Ultra wideband orthogonal frequency division multiplexing (UWB-OFDM) is a novel multi-carrier UWB communication system for high bit rate wireless networks. It uses OFDM modulation with a frequency hopped pulse train in order to achieve full multipath diversity in multipath fading channels. In this paper we consider the problem of simultaneously operating piconets in the IEEE 802.15.3a WPAN standard, or multi-users in an infrastructure wireless local area network (WLAN). We propose a multi-user interference-free (MUI-free) frequency hopping multiple access scheme based on orthogonal set of Costas sequences. We also study other multiple access schemes such as orthogonal frequency division multiple access (OFDMA) and time hopping code division multiple access (TH-CDMA) for UWB-OFDM system.
A novel Sense-Transmit-Wait strategy for coexistence of Cognitive Radio networks with IEEE 802.11 WLANs

Detection of spectral white space and the adoption of optimal secondary user transmission policies are the two crucial aspects of a Cognitive Radio system. In this paper we study the problem of optimal utilization of time domain holes in an unsaturated IEEE 802.11 network. We also design a scheme for the coexistence of a secondary system with an IEEE 802.11 network such that it does not affect the Medium Access Control (MAC) layer behavior of the network by mitigating the infamous “Capture Effect”. Specifically, we propose a Sense-Transmit-Wait strategy which allows the secondary user to effectively decide how often to transmit, when to transmit and the duration for which to transmit while adhering to strict interference constraints imposed via an upper bounded Probability of Collision (p0). Through extensive simulations and analysis of real world 802.11 packet captures, we demonstrate the ability of our proposed system to successfully setup a cognitive network and deliver a significant throughput enhancement.
Performance analysis of directional beacon based position location algorithm for UWB systems

We propose an enhanced directional beacon based position location procedure for UWB systems. Unlike previous work that relies on strongest return and therefore prone to errors in obstructed line of sight (OLOS) environments, our procedure identifies the LOS return by detecting the earliest arrival. To overcome synchronization problems, we propose a correlation based window algorithm to detect the earliest arrival and hence the LOS component across a 360 deg rotation of directional beacon. We performed a detailed and exact analysis of the algorithm that has not yet reported in the literature. The analysis quantifies the dependence of the algorithm on the variance of noise and the beam pattern. The analysis also motivated us to mitigate the effect of noise. We, therefore, propose an improved algorithm and implement it in a computationally efficient way. To improve position estimation we perform a three point search around the estimated time index n and selects the point that minimizes l2 norm of a performance measure vector. From simulation results we claim position location accuracy of around 30 cm with a pragmatic 4-element antenna array using 2.4 GHz UWB pulses and 4-bit A/D converter at 28 dB SNR
Real time tracking of exterior and interior organ surfaces using sparse sampling of the exterior surfaces

This paper presents a new algorithm for real time tracking of the exterior and interior surfaces of organs using sparse sampling of the exterior surfaces. The tracking is based on identifying subspaces in which the coefficients of spherical harmonic representations of the surfaces live. It uses pre operative CT/MRI scans during training, and needlescopic images acquired during tracking. We study different strategies for sampling the exterior organ surface using the needlescopic images and also apply the method to 3D frame interpolation. Specially, we provide (1)the first demonstration of real time interior organ surface reconstruction using sparse sampling of exterior surfaces with error rate as low as 0.095%, and (2) algorithms' application in 3D cardiac frame interpolation with error rate of only 1.15%while reducing radiation rate by 90%.
Deterministic and iterative solutions to subset selection problems

Signal decompositions with overcomplete dictionaries are not unique. We present two new approaches for identifying the sparsest representation of a given signal in terms of a given overcomplete dictionary. The first approach is an algebraic approach that attempts to solve the problem by generating other vectors that span the space of minimum dimension that includes the signal. Unlike other current techniques, including our proposed iterative technique, this algebraic approach is guaranteed to find the sparsest representation of the signal under certain conditions. For example, we can always find the exact solution if the size of the dictionary is close to the size of the space or when the dictionary can be represented by a Vandermonde matrix. Although our technique can work for high signal-to-noise cases, the exact solution is only guaranteed in noise-free cases. Our second approach is iterative and can be applied in cases where the algebraic approach cannot be used. This technique is guaranteed to achieve at least a local minimum of the error function representing the difference between the signal and its sparse representation.
Classification techniques for recurrent DNA copy number data

Genetic instabilities in the human genome are frequently exhibited in the form of DNA copy number variations. In this paper, we propose a framework to evaluate the predictive power of recurrent copy number variations at multiple genomic sites for detecting genetic diseases. Moreover, we compare the ability of two well known clustering algorithms, k-means and Fuzzy c-means, to correctly classify diseased samples with respect to the control samples. Finally, we apply our proposed techniques on 51 samples of 25 apparently healthy and 26 autistic children. Our results show that using CNVs at multiple sites will considerably increase classification performance when compared with the traditional classifiers that focus on a single CNV.
Theory of true-velocity duplex imaging using a single transducer

We study the B-mode/Doppler duplex imaging problem. We begin by looking at the problem of determining the true velocity vector only. We develop a model for the power spectrum of a signal reflected by a line of point scatterers with a Poisson distribution. We show that with circularly symmetric apertures it is possible to use the expression of that power spectrum to determine the true velocity vector from a single excitation and two measurements. We also describe an illumination configuration that guarantees that the velocity estimation process is range-invariant. We conclude the paper by studying the problem of simultaneously estimating the range and true velocity of a flow. In particular, we show that this problem is completely characterized by a generalized range-2-D Doppler ambiguity function that depends on the excitation signal and the transducer geometry.
Transmitter architecture for pulsed OFDM

A multi-band OFDM UWB system is the leading proposal for the new IEEE 802.15.3a standard for wireless high bit-rate personal area networks. Pulsed OFDM is an enhancement to the multi-band OFDM system that offers better performance with lower complexity and a correspondingly reduced power consumption. In this paper, the implementation issues and pipeline struchres of a pulsed OFDM transmitter are presented. In particular, architectural features of the system and its components are described and analyzed.
Detection of blood perfusion

Ultrasound detection of capillary blood flow is difficult as capillary blood flow provides a weak backscattered signal with a small Doppler shift. To aid in the detection of such signals, a new algorithm is introduced exploiting two powerful ideas. First, the receiver is developed by creating subspaces corresponding to expected flow and clutter signals. It then exploits the fact that some of the basis vectors in the flow and clutter subspaces are very nearly perpendicular. By projecting the input data onto the perpendicular components of the flow subspace a very sensitive detector can be realized. Secondly, the detector coherently sums inputs from several angles. In vitro ultrasound data is collected and applied to the detector. The algorithm utilizing multiple angles is shown to be sensitive detecting flow in the experimental data down to 5 mm/sec in a 6 mm diameter channel.
Wavelet transform domain LMS algorithm

A novel normalized wavelet domain least-mean-square (LMS) algorithm is described. The faster convergence rate of this algorithm as compared with time-domain LMS is established. The wavelet domain LMS algorithm requires only real arithmetic. In its most basic form it has a computational complexity that is higher than that of the traditional LMS technique by a factor of cN, where N is the length of the transformed vector (or sliding analysis window) and c is the length of the analysis wavelet. Other preconditioning strategies that yield a faster convergence rate for a given fixed excess mean squared error are discussed. The authors also describe low-complexity implementations of the wavelet domain LMS algorithm. These implementations exploit the structure of the wavelet transform of the underlying stochastic process.<<ETX>>
Fast magnetic resonance imaging via frequency domain wavelet transforms

The technique is based on a proper design of the excitation profile. In particular, the excitation profile is chosen to be highly regular along its boundaries. It is shown that the free induction decay (FID) signals that result from such excitations decay much faster than those produced by traditional MRI approaches as long as the underlying magnetization and a number of its derivatives are continuous. The proposed technique exploits this fact together with an exact model of the slowly decaying components of the FID signal (those produced by discontinuities in the magnetization or its derivatives) to produce fast high-quality reconstructions of the underlying magnetization. Experimental results obtained with a 4-T whole body scanner are presented to demonstrate the viability of this approach.<<ETX>>
Fast positive definite linear system solvers

The authors show that the M-band wavelet transforms of a wide class of covariance matrices consist of subblocks that are essentially banded. Furthermore, they prove that the Cholesky factors of the transformed covariance matrices also consist of subblocks that are essentially banded. They combine these two observations to construct a fast O(N/sup 2/) algorithm for solving the N/spl times/N linear positive definite systems of equations that arise in statistical signal processing. Finally, they provide an error analysis of the proposed linear positive definite system solver.