nils-herrmann/clean_scirepeval huggingface dataset:
Distributed AR spectral estimation

The author studies the problem of combining in an optimal fashion autoregressive (AR) model-based spectral estimates obtained at geographically dispersed sites under the assumptions that the data are Gaussian AR(p), all sites fit the data to a p-order all-pole model where p is a given integer known to all sensors and to the fusion center, and the amount of data collected at each site is large. It is shown that the optimal centralized AR-model-based spectral estimates can be computed from the knowledge of each local spectral estimate and the number of data points that were used at each site to obtain the local estimate. An approximation to the optimal centralized AR-model-based estimate is computed by solving a set of linear equations followed by an application of the Levinson-Durbin algorithm.<<ETX>>
Real time tracking of 3D organ surfaces using single MR image and limited optical viewing

This paper presents the first demonstration of real time 3D tracking of organ deformation based on one-sided, limited view needlescopic optical imaging and a single pre-operative MRI/CT scan. The reconstruction is based on the empirical observation that the spherical harmonic coefficients corresponding to distorted surfaces of any given organ lie in lower dimensional spaces that can be learned during training. The paper discusses the details of the selection of the limited optical views and the registration of the real time partial optical images with the single pre-operative MRI/CT scan. Finally, it demonstrates the first experimental 3D reconstruction of ex-vivo kidneys based on a single MRI scan with 1 mm resolution and real time single side optical imagery achieving spatial resolution of better than 2 mm, even on the hidden organ surface, or less than 1.85% relative error.
Distributed Robust Biclustering Algorithm for Gene Expression Analysis

Show by Cheng and Church to be an NP-complex problem, biclustering algorithms are more complex than the classical one dimensional clustering technique, particularly requiring multiple computing platforms for large and distributed datasets. In this study, we proposed and extension of the robust biclustering algorithm (RoBA) that is capable of performing biclustering on extremely large or geographically distributed set of gene expression data. The distributed version will divide the cluster tasks among A' processors with negligible communication costs thus making it scalable over large number of computing nodes. The proposed algorithm has been implemented using Matlab MPI and the performance results are reported based on executions on a 1, 2, 3, 4, and 5 nodes Windows PC cluster connected over 100 Mbits links. The experimental results show increased performance with the increased number of nodes on the same set of data.
Detection of weak signals using adaptive stochastic resonance

We present a novel nonlinear filtering approach for detecting weak signals in heavy noise from short data records. Such detection problems arise in many applications including communications, radar, sonar, medical imaging, seismology, industrial measurements, etc. The performance of a matched filter detector of a weak signal in heavy noise is directly proportional to the observation time. We discuss an alternative detection approach that relies on a nonlinear filtering of the input signal using a bistable system. We show that by adaptively selecting the parameters of the system, it is possible to increase the ratio of the square of the amplitude of a sinusoid to that of the noise intensity around the frequency of the sinusoid (stochastic resonance). The sinusoid can then be reliably detected at the output of the nonlinear system using a suitable matched filter even when the data record is short.
Color halftone document segmentation and descreening

The advent of electronic publishing creates strong interest in converting existing printed documents into electronic formats. During this process, image reproduction problems can occur due to the formation of moire patterns in the screened halftone areas. A wavelet packet based color halftone segmentation algorithm is first designed to locate possible halftone regions based on a decision function. A color halftone descreening technique based on the color sigma filter is proposed which does not assume any a priori knowledge about the halftoning process, making it applicable to any color halftone image. Combined with color halftone segmentation techniques, a complete document processing algorithm for color documents is proposed. Experimental results are offered to illustrate the performance of our algorithm.
Search strategies for radar target localization

In this paper we discuss and compare different hierarchical search strategies for stationary radar target localization in the presence of white Gaussian noise. This is done in the context of a discretized version of the problem of optimal beam-forming, or radar transmit and receive pattern design. We assume that the target is equally likely to be in one of M discrete cells. We compare the performance of search strategies using the proposed approaches for beam-form design in target localization problems.
In-Home Assistive System for Traumatic Brain Injury Patients

We describe a system for assisting patients in a home setting who suffer from cognitive impairments due to traumatic brain injury. The system integrates fixed wireless home sensors and wearable wireless sensors. We focus on the task of classifying activities of daily living. We locate and track the subjects with the help of home sensors and capture the details of an executed activity with a 2-axis wearable wireless accelerometer sensor attached to the right wrist. We extract time domain and frequency domain features for each task and classify them with Gaussian mixture models followed by a majority voter. The majority voter provides low false positive rates while continuously tracking the tasks. The experimental results from 2 subjects in recognizing 4 distinct daily activity tasks are promising.
On the equivalence of uniform circular arrays and uniform linear arrays

The problem of estimating the directions of arrivals of narrowband plane waves impinging on a uniform circular array with N identical sensors uniformly distributed around a circle is considered. It is shown that for this problem the uniform circular array is equivalent to a uniform linear array with N elements. Specifically, if the number of sensors N is odd and large enough then a linear combination of the inverse discrete Fourier transform of the sensor outputs yields a sequence of measurements z(n)= Sigma A/sub m/ exp(jn theta m), where theta /sub m/ is the angle of arrival of the mth plane wave. This sequence can then be processed using ROOT MUSIC or any other modern line spectral estimation technique as if it came from a uniform linear array. An advantage of using the transformed sequence is that it has a higher signal-to-noise ratio than the array output sequence.<<ETX>>
When seeing isn't believing [multimedia authentication technologies]

Digital multimedia is ubiquitous today. Multimedia is easily reproduced and modified without any trace of manipulations. In most cases, a human will not be able to judge whether a multimedia signal is authentic by perceptual inspection. In this article we provide a compact yet fairly comprehensive introduction of multimedia authentication (MA) to the general signal processing audience. The article gives a brief discussion on the different MA technologies such as hard authentication, soft authentication, quality-based authentication, content-based authentication, block authentication, and lossless watermarking.
An efficient detection on capacitive touch screens using bandwidth expansion

This paper presents a new efficient detection method on capacitive touch screens by mixing multi-channel inputs and expanding the sensing bandwidth. Multi-channel touch signals can be combined with a modulation scheme which allows an overlap in spectral domain. By expanding the sensing bandwidth, the overlapped region can be minimized which leads to more accurate detection. The touch-detection problem is formulated as an event detection problem which decides touch-input status under Gaussian noise. We provide the numerical analysis of the proposed scheme, and show the relation between the detection performance and the bandwidth expansion rates. The simulation result confirms that slight increase in sensing bandwidth yields an accurate touch detection using the proposed sensing and detection schemes.
Expert computer vision based crab recognition system

Two species of crabs are Chionoecetes bairdi and C. opilio and their hybrids live in the Bering Sea. The two species differ in generic, morphological and morphometric characteristics. Inter breeding of C. bairdi and C. opilio results in a hybrid form with intermediate morphological and morphometric characteristics. These species were determined by analyzing the empirical covariance matrices associated with the two species and the hybrid and by taking into account the statistical reliability of the estimated principal components. A modified eigen image classifier is implemented based on the assumption that the data is drawn from multivariate Gaussian distributions with different means and covariance matrices. The clustering technique is introduced to minimize the misclassification rate of the eigen image classifier.
Learning local vessel appearance models using structured sparsity

Vessel segmentation is a challenging task due to the complexity of vascular networks and limitations of imaging modalities to accurately capture thin structures. Analytical models based on geometric appearance and/or edge-based assumptions have been shown to be sub-optimal in segmenting vessels. In this paper, a novel approach for learning vessel appearance models from localized-vessel image patches is presented. This approach uses subspace clustering methods based on sparse representation of signals to identify local vessel appearance models from a training dataset. This paper also presents a hierarchical subspace clustering framework, which improves clustering speeds in presence of large number of subspaces. The preliminary results obtained for segmenting retinal vessel images using learned appearance models on the publicly available DRIVE database, yields an accuracy of 0.9268 at 0.1344 false detections and reduces the search space up to 80%.
Active contour based rock sole recognition

In this paper, we describe a system for classifying rock sole fish. The contour of the fish and existence of a stripe pattern constitute two important features to distinguish between different subspecies of rock sole. The system uses a multiscale sigma filter and active contour algorithm to extract the contour of the groundfish. The extracted contour is then transformed into a one dimensional signature to serve as the first feature to differentiate the northern rock sole from the southern rock sole. Next, our system uses a pattern enhancing algorithm and transforms the extracted pattern to the spatial frequency domain to serve as the second feature. A matched subspace filter is then applied to recognize each feature. The proposed recognition system integrates the classification result from both channels. We illustrate the performance of the procedure on sample images of northern and southern rock soles.
Fast polynomial regression transform for video database

Important issues in multimedia information systems are the development of efficient storage layout models and effective retrieval system to manage multimedia databases. In multimedia information systems, the capability for interaction with video media type is still extremely limited due to a huge amount of video databases. We present the Fast Polynomial Regression Transform (FPRT) based metadata scheme for manipulating the large video database in the paper. This new metadata scheme provides the simplification of the query mechanism and the improvement of communication in multimedia systems. The main advantage of the proposed approach is the reduction of the data storage space while preserving much video content information. Other advantages are computational simplicity for video information coding and accuracy for video indexing. Visual tools for video representation and browsing are also presented.
Low complexity dynamic region and translational motion estimation for video indexing

A new low complexity approach to motion estimation for video indexing is proposed. The fast polynomial regression transform (FPRT) is utilized to provide the efficient storage layout model and effective video database for manipulating the multimedia information system. The video content information of video image is compressed by FPRT. Global translational motion vectors and location of dynamic regions are computed from the compressed data. Our experiments show that this approach is very efficient for fast motion based video indexing.
Reduced complexity bounded error subset selection

A reduced complexity version of the bounded error subset selection (BESS) algorithm is proposed. By relaxing the integer constraint in the original BESS algorithm, we show that the BESS problem can be reformulated as an ordinary linear program instead of an integer program with exponential worst-case complexity. We retain the sparseness of the representation in the modified BESS by weighting the dictionary with the minimum 2-norm solution of the subset selection problem corresponding to the BESS problem at hand. The proposed algorithm is compared to the basis pursuit, orthogonal matching pursuit, and the best orthogonal basis algorithms. It is shown that the proposed algorithm has a better packing property and an improved rate-distortion behavior.
Design and analysis of post-coded OFDM systems

This paper discusses the design and analysis of post coded OFDM (PC-OFDM) systems. Coded or precoded OFDM systems are generally employed to overcome the symbol recovery problem in uncoded OFDM systems. We show that PCOFDM systems are a special case of precoded OFDM systems that offer advantageous complexity-performance trade-offs. In particular, PC-OFDM systems introduce frequency diversity by manipulating the OFDM symbols in the time domain so that the computational complexity of the system can be significantly reduced. We discuss the design principles of PC-OFDM transmitter that uses upsampling operation and the spreading codes to introduce frequency diversity. We obtain the spreading code construction criterion for minimum error performance and give examples of spreading codes for PC-OFDM systems. We also describe the design of low-complexity receiver for PC-OFDM systems. In particular, our proposed partial spreading scheme results in a low complexity decoupled detector. The probability of error analysis of the receiver leads us to postulate different design criteria. We investigate different choices for detection algorithms suitable for PC-OFDM receiver and compare their performance through simulations over Rayleigh and IEEE UWB channels.
Averaged acoustic emission events for accurate damage localization

Localizing micro cracks in critical components is crucial in the field of continuous structural health monitoring. In this paper, we utilize several signal processing and machine learning techniques such as hierarchical clustering and support vector machines (SVM) to process multisensor acoustic emission (AE) data generated by the inception and propagation of cracks. We present preliminary laboratory results that explore the pairwise event correlation of AE waveforms generated in the process of controlled crack propagation, and use these characteristics for clustering AE. By averaging the AE events within each cluster obtained from hierarchical clustering, we compute super-acoustics with higher signal to noise ratio (SNR) and use them in the second step of our analysis for calculating the time of arrival information (TOA) for crack localization. We utilize a SVM classifier to recognize the so called P-waves in the presence of noise by using features extracted from the frequency domain for accurate earliest arrival detection. Preliminary results show that our method has the potential to be a component of a structural health monitoring system based on acoustic emissions for instance for bridges.
Overcoming Long-Term Variability in Local Field Potentials Using an Adaptive Decoder.

Long-term variability remains one of the major hurdles in using intracortical recordings like local field potentials for brain computer interfaces (BCI). Practical neural decoders need to overcome time instability of neural signals to estimate subject behavior accurately and faithfully over the long term. This paper presents a novel decoder that 1) characterizes each behavioral task (i.e., different movement directions under different force conditions) with multiple neural patterns and 2) adapts to the long-term variations in neural features by identifying the stable neural patterns. This adaptation can be performed in both an unsupervised and a semisupervised learning framework requiring minimal feedback from the user. To achieve generalization over time, the proposed decoder uses redundant sparse regression models that adapt to day-to-day variations in neural patterns. While this update requires no explicit feedback from the BCI user, any feedback (explicit or derived) to the BCI improves its performance. With this adaptive decoder, we investigated the effects of long-term neural modulation especially when subjects encountered new external forces against movement. The proposed decoder predicted eight hand-movement directions with an accuracy of 95% over two weeks (when there was no external forces); and 85% in later acquisition sessions spanning up to 42 days (when the monkeys countered external field forces). Since the decoder can operate with or without manual intervention, it could alleviate user frustration associated with BCI.
Performance Synthesis of UAV Trajectories in Multistatic SAR

We develop guidelines for the design and analysis of unmanned aerial vehicle (UAV) trajectories for the purpose of high-quality image reconstructions based on a tomographic framework for a downlooking multistatic SAR imaging system. Modern matrix analysis provides several tools to generate performance bounds, which we relate to physical parameters of the synthetic aperture radar (SAR) measurement process. Both deterministic and random trajectories are considered. We apply a central result of random matrix theory to describe the system performance of random-location sampling.
Analysis of higher-order N-tone sigma-delta modulators for ultra wideband communications

We consider the properties of a class of non-oversampling N-tone sigma-delta modulators which have applications in the design of UWB-OFDM communications systems. The spectrum gaps that exist in such systems are well-matched to the noise shaping properties of these modulators and their non-oversampling nature makes them practical for use with these ultra wideband signals. Performance results for first-order, second-order and L/sup th/-order modulators are presented, and a general expression for the excess resolution that can be gained in such systems is obtained.
Empirical Likelihood Ratio Test With Distribution Function Constraints

In this work, we study non-parametric hypothesis testing problem with distribution function constraints. The empirical likelihood ratio test has been widely used in testing problems with moment (in)equality constraints. However, some detection problems cannot be described using moment (in)equalities. We propose a distribution function constraint along with an empirical likelihood ratio test. This detector is applicable to a wide variety of robust parametric/non-parametric detection problems. Since the distribution function constraints provide a more exact description of the null hypothesis, the test outperforms the empirical likelihood ratio test with moment constraints as well as many popular goodness-of-fit tests, such as the robust Kolmogorov-Smirnov test and the Cramér-von Mises test. Examples from communication systems with real-world noise samples are provided to show their performance. Specifically, the proposed test significantly outperforms the robust Kolmogorov-Smirnov test and the Cramér-von Mises test when the null hypothesis is nested in the alternative hypothesis. The same example is repeated when we assume no noise uncertainty. By doing so, we are able to claim that in our case, it is necessary to include uncertainty in noise distribution. Additionally, the asymptotic optimality of the proposed test is provided.
Reusable dynamic programming:Updating sequence alignment

Sequence alignment in genomics and proteomics is mostly done via dynamic programming (DP) based approaches. In this work, we show how computational results from DP can be reused to update alignments when analyzing new versions of a sequence. We derive relative tolerance bounds on node distances from a root node that guarantee that partial shortest path distances remain optimal. We then propose an algorithm that uses these bounds to skip all unperturbed parts of a sequence when recomputing an alignment. We also discuss techniques to reduce the memory requirements of the algorithm by focusing on the highly conserved segments of the sequence. Experimental results establish that our proposed alignment procedure can update alignment decisions of modified sequence with 4.6% to 18% of the number of computations required by the normal Needleman-Wunsch algorithm, depending on sequence length. Higher computational savings are achieved with longer sequences.
An efficient video broadcasting protocol with scalable preloading scheme

We present a new video broadcasting protocol called the scalable preloading broadcast protocol. This protocol provides cost effective access times to viewers by using a scalable preloading method. The new protocol reduces the video stream management complexity as well as the required broadcasting bandwidth. To demonstrate the benefit of new protocol, we perform various simulations to compare its performance with that of previous broadcasting protocols. With simulation results, we show that our protocol's required bandwidth is very close to the theoretical minimum throughout all client waiting times. Furthermore, by comparison with previous broadcasting protocols, we also show how our broadcasting protocol can dramatically reduce the video stream decoding complexity.
Blind identification of single-input multiple-output pole-zero systems

In this paper we present a technique for the blind identification of single-input multiple-output (SIMO) pole-zero (PZ) systems using only second order statistics of the system output data. The system input is treated as an unknown deterministic sequence, and hence, restrictive i.i.d. Assumptions on the input sequence are not required. We estimate the poles and zeros of the channels in two steps: (1) estimate the product of all permutations of a numerator and a denominator polynomial from two different channels, and (2) extract individual numerator and denominator polynomials for each channel from the above estimate. Our technique performs well even with short records of data.