nils-herrmann/clean_scirepeval huggingface dataset:
Using topographical channel distribution to decode movement directions from Local Field Potentials

In Brain Machine Interface (BMI), movement direction can be decoded using intra-cortical recordings such as Local Field Potentials (LFP). Due to the natural instability and non-stationarity of these recordings, it is difficult to develop decoders that remain consistent over time and are not affected by learning. This paper uses qualitative information based on the temporal and spatial distribution of inter-channel ranking. The image block processing technique is exploited, and the distribution of top ranked channels is calculated. We use this spatio-temporal distribution information to decode the movement direction via a maximum likelihood estimator. Our results indicate that the decoding power is consistent over a period of two weeks. On an average, we obtain an average classification accuracy of 51.9% versus 33.2% from traditional state-of-the-art technique over a two week period.
Ambulatory device for urinary incontinence detection in females

This paper describes the design and use of an ambulatory monitoring device for recording urological response to intense physical activities of women. The system integrates a tri-axial accelerometer, a 360 degree biaxial inclinometer and a specially designed urine leakage detector(ULD) for sensing body motion and urine discharge. The device is small, lightweight and battery powered, and can be worn comfortably. All recordings are taken non-invasively and transmitted wirelessly to a receiver for real-time data logging. The experimental results show that the proposed system can record acceleration, inclination efficiently and detect urine leakage of amounts from 0.5ml to 10ml accurately. The unique design of the ULD sensor exempts it from sweating interference during vigorous activities.
A cross-correlation approach to astronomical speckle imaging

The problem of reconstructing finite support objects from images degraded by a propagation through a turbulent atmosphere has been treated. A method for reconstructing the cross-correlating short exposure images of the object with short exposure images of a reference star has been developed. This makes it possible to obtain both magnitude and phase information about the Fourier transform of the object intensity up to the diffraction limit of the telescope. The derivation assumes that the complex amplitude of light that has propagated through the atmosphere has Gaussian statistics in the telescope pupil. In this approach the object intensity is the solution of a Fredholm integral equation of the first type with a positive semidefinite rotationally symmetric kernel. Furthermore, the low-pass nature of the instantaneous point spread function of the atmosphere/telescope and the finite spatial extent of the recorded short exposure images imply that the object intensity cannot be exactly reconstructed without some a priori information.<<ETX>>
Convolutional decoding in the presence of false alarms

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.
Sampling theorems for two-dimensional isotropic random fields

Sampling theorems are developed for isotropic random fields and their associated Fourier coefficient processes. A wave-number-limited isotropic random field z(r) is considered whose spectral density function is zero outside a disk of radius B centered at the origin of the wavenumber plane. z(r) can be reconstructed in the mean-square sense from its observation on the countable number of circles with radius r/sub i/=i pi /B, i in N, or of radius r/sub i/=a/sub i,n//B, i in N, where a/sub 1,n/ denotes the ith zero of the nth-order Bessel function J/sub n/(x), and n is arbitrary.
Automatic characterization and detection of behavioral patterns using linear predictive coding of accelerometer sensor data

In this study, we target to automatically detect behavioral patterns of patients with autism. Many stereotypical behavioral patterns may hinder their learning ability as a child and patterns such as self-injurious behaviors (SIB) can lead to critical damages or wounds as they tend to repeatedly harm one single location. Our custom designed accelerometer based wearable sensor can be placed at various locations of the body to detect stereotypical self-stimulatory behaviors (stereotypy) and self-injurious behaviors of patients with Autism Spectrum Disorder (ASD). A microphone was used to record sounds so that we may understand the surrounding environment and video provided ground truth for analysis. The analysis was done on four children diagnosed with ASD who showed repeated self-stimulatory 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. The goal of this study is to devise novel algorithms to detect these events and open possibility for design of intervention methods. In this paper, we have shown time domain pattern matching with linear predictive coding (LPC) of data to design detection and classification of these ASD behavioral events. We observe clusters of pole locations from LPC roots to select candidates and apply pattern matching for classification. We also show novel event detection using online dictionary update method. We show that our proposed method achieves recall rate of 95.5% for SIB, 93.5% for flapping, and 95.5% for rocking which is an increase of approximately 5% compared to flapping events detected by using wrist worn sensors in our previous study.
Perfectly balanced binary sequences with optimal autocorrelation

We have found in our recent work that the coding gain of a coded OFDM system can be maximized if the out-of- phase autocorrelation of the encoding sequence is zero for initial lags. The number of lags with zero autocorrelation depends on the channel length. We are interested in unbiased sequences and consider perfectly balanced binary sequences (BBSs) only. Our main goal in this paper is to obtain BBSs with best possible autocorrelation. We also study the properties of difference sets associated with BBSs and prove the non-existence of BBSs under commonly used difference sets.
On the application of uniform linear array bearing estimation techniques to uniform circular arrays

The problem of estimating the directions of arrival of narrowband plane waves impinging on a uniform circular array with M identical sensors uniformly distributed around a circle is considered. Specifically, the authors study a transformed data sequence that is equal to a reordering of the inverse discrete Fourier transform sequence corresponding to the outputs of a circular array with M elements uniformly distributed around the array circumference. It is shown that the transformed sequence may be processed using a ROOT MUSIC based approach to estimate the elevations and azimuths of the observed sources. Any root estimated via ROOT MUSIC which is on or close to the unit circle indicates the presence of a source at the elevation under consideration and an azimuth equal to the phase of the root. Experimental results are provided to demonstrate the advantages of processing the transformed data.
Receiver structures for multicarrier UWB systems

Multicarrier UWB (MC-UWB) communication systems use orthogonal UWB pulse trains and multiple sub-channels to achieve reliable high bit rate transmission and spectral efficiency. In a multipath fading channel, MC-UWB systems suffer from inter-channel interference (ICI) because the received sub-carriers arriving via different paths are no longer orthogonal. We study the performance of the optimum multichannel maximum likelihood (MC-ML) receiver for such channels. The complexity of the optimum receiver increases exponentially with the number of sub-carriers. Since the optimum receiver is too complex to be implemented, we also study a single channel Rake receiver with channel information and a noncoherent single channel receiver that requires no channel information. We compare their performance to that of the optimal receiver and that of a pilot assisted scheme for MC-UWB.
A scheduled broadcasting protocol for efficient video on demand

Broadcasting protocols can improve the efficiency of video on demand services by distributing videos that are likely to be simultaneously viewed by many clients. We have developed an efficient video broadcasting protocol called the scheduled broadcasting protocol. This protocol reduces the complexity of managing incoming streams as well as the bandwidth required to broadcast. To assess the benefit of the new protocol, we performed various simulations to compare its performance with that of previous broadcasting protocols. The simulation results show that our protocol's required bandwidth is very close to the theoretical minimum for all client waiting times. Furthermore, comparison with previous broadcasting protocols shows how our broadcasting protocol can dramatically reduce both the number of simultaneous streams during the video duration and that of required modules for watching a video.
Space-invariant true-velocity flow mapping using coplanar observations

The diagnostic value of the current two-dimensional Doppler flow mapping techniques is limited by the fact that velocity values at different parts of the image are only projections of the total three-dimensional true-velocity vectors onto the image plane. Since these vectors have different orientations, the resulting values do not have the same reference and hence cannot be compared for correct diagnosis. Here, the authors show that it is possible to obtain true-velocity flow maps from coplanar observations. This effectively eliminates any angle dependency in the imaging process and provides a convenient way of representing velocity as a two-dimensional map relative to the same reference. Also, the authors establish the conditions under which this estimation process is space invariant to illustrate the practicality of the technique.
Collision resolution in pseudocellular WLANs with beamforming

The paper provides a technique that can successfully recover data when collision occurs in pseudocellular wireless local area networks (WLANs). Pseudocellular WLANs use beamforming at the access point (AP). Mobile users send multiple data packets at the same time using different signature waveforms as in CDMA cellular network. Each user randomly chooses a signature waveform during the contention frame. If the same signature waveforms is selected by more than one user, a collision occurs. The proposed successive multiuser detection scheme with orthogonal ID sequence design enables the system to recover the data from collision. This helps to prevent mobile users from starting back-off timing due to failure in contention for the channel. Hence the total delay is lowered and the capacity of the system improves by a large amount under heavy traffic.
Parametrization of compactly supported orthonormal wavelets

The parametrization of two-channel reconstruction quadrature mirror filter banks is extended to obtain a single characterization of discrete orthonormal wavelets. In particular, it is shown that discrete orthonormal wavelets of support less than 2N-1, where N is an even integer, are parametrized by N-1 real parameters, each taking values in (0, 2 pi ). A parametrization of discrete orthonormal wavelets with a number of vanishing moments greater than one is also given.
Predictive value of recurrent DNA copy number variations

Recurrent copy number variations across multiple samples are increasingly used to identify the genes and the genomic locations that are statistically and biologically significant and correlated with certain diseases. In this paper, we evaluate the predictive power of copy number variations for detecting autism. We consider both recurrent copy number variations at one location and correlated recurrent copy number variations at multiple locations. In each case, we compare the ability of k-means and Fuzzy c-means algorithms to correctly classify autistic samples. Finally, we apply our proposed techniques on 51 samples of 25 apparently healthy and 26 autistic children.
Wavelet transform domain adaptive FIR filtering

This paper presents and studies two new wavelet transform domain least mean square (LMS) algorithms. The algorithms exploit the special sparse structure of the wavelet transform of wide classes of correlation matrices and their Cholesky factors in order to compute a whitening transformation of the input data in the wavelet domain and minimize computational complexity. The procedures differ in the exact estimates they use and in the way they identify the data dependent whitening transformation. The first approach explicitly computes a sparse estimate of the wavelet domain correlation matrix of the input process. It then computes the Cholesky factor of that matrix and uses its inverse to whiten the input. The complexity of this first approach is O[N log/sup 2/ (N)]. In contrast, the second approach computes a sparse estimate of the Cholesky factor of the wavelet domain correlation matrix of the input process directly. This second approach has a computational complexity of O[N log (N)] floating-point operations. However, it requires a more complex bookkeeping procedure. Both algorithms have a convergence rate that is faster than that of time-domain LMS and discrete Fourier transform (DFT) or discrete cosine transform (DCT)-based LMS procedures. The paper compares the two procedures and analyzes their mean and mean square performance.
Distributed sensor network localization using SOCP relaxation

The goal of the sensor network localization problem is to determine positions of all sensor nodes in a network given certain pairwise noisy distance measurements and some anchor node positions. This paper describes a distributed localization algorithm based on second-order cone programming relaxation. We show that the sensor nodes can estimate their positions based on local information. Unlike previous approaches, we also consider the effect of inaccurate anchor positions. In the presence of anchor position errors, the localization is performed in three steps. First, the sensor nodes estimate their positions using information from their neighbors. In the second step, the anchors refine their positions using relative distance information exchanged with their neighbors and finally, the sensors refine their position estimates. We demonstrate the convergence of the algorithm numerically. Simulation study, for both uniform and irregular network topologies, illustrates the robustness of the algorithm to anchor position and distance estimation errors, and the performance gains achievable in terms of localization accuracy, problem size reduction and computational efficiency.
Detection of known and unknown signals, over fading channels

This paper first addresses the problem of known signal detection over both Rayleigh and Nakagami fading channels. More specifically, it studies the effect of different diversity schemes as a remedy to improve the probability of detection. Maximum ratio combining, selection combining, and switch and stay diversity schemes over Rayleigh fading channels are investigated. Closed form expressions for the average probability of detection for no diversity and diversity cases are obtained. The second part of this paper deals with both generalized likelihood ratio and uniformly most powerful tests for unknown-signal detection over Rayleigh fading channels. For this purpose, a simple decision criterion assuming only the knowledge of the noise power spectral density and the signal bandwidth is provided.
Optimal information ordering for sequential detection with cognitive biases

The manner and order in which data is presented to a human observer can lead the human to make dramatically different decisions. This raises a question on how to best present data to an observer to achieve the best decision-making performance and minimum adverse effects. In this paper, we present a general framework to model cognitive biases that interfere in the human decision making process. We examine the problem of ordering observations in binary sequential detection. Our treatment considers the limited cognitive effort exerted by a decision-maker and the effect of the observations along with their distributions on the stopping time and accuracy of the sequential test. The complexity of the ordering algorithm is linear in the size of the observation set. Both the average time to make a decision and the probability of decision error are minimized.
Maximum likelihood estimation of the fractal dimensions of stochastic fractals and Cramer-Rao bounds

A maximum likelihood (ML) estimator for the parameters of Gaussian versions of the fractionally differenced white noise process is developed. A closed-form expression for the likelihood equation is given, and Cramer-Rao bounds are computed for finite size sample data sets. It is shown how the theory can be extended to the case where the fractionally differenced white noise process is observed in the presence of white noise. The results obtained with this ML approach are satisfactory, with a mean square error which is very close to the theoretically computed Cramer-Rao bound.<<ETX>>
Affine-invariant multiresolution image retrieval using B-splines

We propose a technique to search through large image collections. Each database image is stored as a combination of potential query terms (i.e., objects, texture regions, etc.) and non-query terms. The query terms are represented by affine-invariant B-spline moments and wavelet transform subbands. The dual representation supports a two-stage image retrieval system. A user-posed query is first mapped to a dictionary of prototype object contours represented by B-spline moments. The B-spline mapping reduces the query search space to a subset of the original database. Furthermore, it provides an estimate of the affine transformation between the query and the prototypes. The second stage consists of a set of embedded VQ dictionaries of multiresolution subbands of image objects. The estimated affine transformation is employed as a correction factor for the multiresolution VQ mapping. A simple bit string matching algorithm compares the resulting query VQ codewords with the codewords of the database images for retrieval.
Video abstract of video

We present a new video summarization procedure that produces a dynamic (video) abstract of the original video sequence. Our approach relies on an adaptive nonlinear sampling of the video. The local sampling rate is directly proportional to the amount of visual activity in localized sub-shot units of the video. The resulting video abstract is highly compact. At playtime, linear interpolation is used to provide the viewer with a summary of the video that accurately preserves the relative length and amount of activity in each sub-shot unit.
Low bit rate transparent audio compression using adapted wavelets

Describes a novel wavelet based audio synthesis and coding method. The method uses optimal adaptive wavelet selection and wavelet coefficients quantization procedures together with a dynamic dictionary approach. The adaptive wavelet transform selection and transform coefficient bit allocation procedures are designed to take advantage of the masking effect in human hearing. They minimize the number of bits required to represent each frame of audio material at a fixed distortion level. The dynamic dictionary greatly reduces statistical redundancies in the audio source. Experiments indicate that the proposed adaptive wavelet selection procedure by itself can achieve almost transparent coding of monophonic compact disk (CD) quality signals (sampled at 44.1 kHz) at bit rates of 64-70 kilobits per second (kb/s). The combined adaptive wavelet selection and dynamic dictionary coding procedures achieve almost transparent coding of monophonic CD quality signals at bit rates of 48-66 kb/s. >
Maximum likelihood principle for DNA copy number analysis

Microarray technologies had been used to measure DNA copy number data. The copy number represents the relative fluorescent intensity level between control and test DNA samples. Variation of this number may lead to many genetic diseases such as cancer. Unfortunately, the observed copy numbers are corrupted by noise due to experimental errors and probes accuracy, making the variations hard to detect. Different techniques had been proposed to denoise the data and to extract the important feature such as the breakpoints from the variant regions. In this paper, we present a robust procedure for the analysis of DNA copy number data based on maximum likelihood principle using global information of the entire data record. We show that Dynamic programming can be used to compute the DNA copy number estimates and reduce the computational complexity. Furthermore, we employ the Minimum Description Length rule to estimate the number of unknown parameters. Using simulated and real data, we show that the proposed method outperforms other popular commercial software and published algorithms.
Random sampling strategies in multistatic SAR

We introduce and investigate the concept of random sampling to the problem of image reconstruction in a multistatic SAR system. We first develop an appropriate figure of merit for the invertibility of the measurement data. We then show numerical simulations of a particular random measurement process under various parameters and compare the performance of this random sampling to that of the sampling given by a deterministic elliptical transform. We expose new connections between SAR processing and random matrix theory, and develop a fundamental result relating image quality to SAR system parameters for this mode.
Localization in nonline-of-sight environments for wireless sensor networks

In this paper we present a ranging algorithm for indoor quasistatic sensor environments based on statistical modeling of the multipath channel. Based on the noise and LOS characterization from this model, we use multiple returns (or signal receptions) at each sensor node to accurately estimate the position of the direct path or line-of-sight (LOS) component in the received multipath signal. This provides range estimates with submeter accuracy. Furthermore, the associated ranging error does not increase with increase in the transmitter-receiver range.
Waveform and beamform design for Doppler ultrasound vector flow mapping

The authors consider the problem of producing an "optimal" range 2-D Doppler flow map in a finite amount of time. A range 2-D Doppler provides estimates of the magnitudes of the axial and two transverse flows in general as a function of range. By optimal image, the authors mean the closest image in a 2-norm sense to the actual the transverse and line-of-sight flows distribution as a function of range. They show that to obtain that optimal image one needs to adjust both the beamform and waveform of the transmitted ultrasound field to the flow distribution. The authors propose a beamform and waveform selection strategy that leads to a progressive refinement of resolution of the produced image along the range, and vector velocity axes. They discuss imaging scenarios in which the returns due to the optimal, beamforms and waveforms can be "synthesized" from those corresponding to a standard beamform and waveform and scenarios where one actually needs to generate different beamforms and waveforms to improve the quality of the final image.