nils-herrmann/clean_scirepeval huggingface dataset:
Sparse representation of deformable 3D organs

Parametric representation of deformable object with complex surface has been a challenge in various medical applications for its demanding resource consumptions. This paper proposed an efficient algorithm to construct a compact basis for a sequence of deformed 3D organ, in which those surfaces can be sparsely represented with a small number of parameters. The key idea in this paper is to explore the correlations among the deformed surfaces of an organ and extract the principle basis for representation and reconstruction. Both theoretical analysis and extensive simulations verified that the presented algorithm yields a three-order magnitude reduction in computational and storage complexity relative to traditional approaches while maintaining high precision for surface reconstruction. The proposed algorithm can be used for organ deformation tracking and optimal sampling strategy design.
Robust clustering of acoustic emission signals using the Kohonen network

Acoustic emission-based techniques are promising for nondestructive inspection of mechanical systems. For reliable automatic fault monitoring, it is important to identify the transient crack-related signals in the presence of strong time-varying noise and other interference. In this paper we propose the application of the Kohonen network for this purpose. The principal components of the short-time Fourier transforms of the data were applied input of the network. The clustering results confirm the capability of the Kohonen network for reliable source identification of acoustic emission signals, assuming enough care has been taken in implementing the training algorithm of the network.
ProTOMAC: Proactive Transmit Opportunity Detection at the MAC Layer for Cognitive Radios

Traditional Cognitive Radio Networks aim to utilize the radio spectrum white space. We present a radically different approach which exploits the excess Signal-to-Noise ratios at which the primary network nodes operate. This paper introduces ProTOMAC (Proactive Transmit Opportunity Detection at MAC Layer), the first spectrum access paradigm that leverages the gray space present at the MAC layer via a novel conceptual extension of the Interference Temperature (IT) approach. The ProTOMAC architecture is based on detecting changes in the primary network packet statistics. Specifically, we develop novel techniques based on iteratively updated information theoretic divergence measures along with rapid and accurate kernel density estimators. These enable the secondary network to operate subject to an interference constraint that ensures a given primary network QOS. ProTOMAC presents a generalized scheme for coexistence of dissimilar Packet Based Networks. We provide an efficient implementation of ProTOMAC using commercial off-the-shelf equipment. The coexistence efficacy of ProTOMAC is validated on an IEEE 802.11g WLAN via establishing a 500 kbps secondary link with a range of 3 meters with an early interference detection time of 240-600 ms. For the first time, identification and exploitation of hitherto unutilized transmission opportunities in Packet Based Networks has been made possible.
An eigenstructure approach to edge detection

A procedure for detecting step edges in noisy signals that does not involve any prefiltering of the data is proposed. It locates step edges in 1-D noisy signals as follows. First, it computes the eigenvectors corresponding to the three smallest eigenvalues of a matrix formed with the discrete Fourier transform of the given data. Next, it estimates the edge locations by finding the local minima in the sum of the spectra of the computed eigenvectors. The technique computes a point edge map for 2-D images by analyzing each row, column, and 45 degrees and 135 degrees diagonal in the image. The computational complexity of the proposed procedure is determined.
A new approach for ICD rhythm classification based on support vector machines

Inappropriate shocks due to misclassification of supraventricular and ventricular arrhythmias remain a major problem in the care of patients with Implantable Cardioverter Defibrillators (ICDs). The purpose of this study was to investigate the ability of a new covariance-based support vector machine classifier, to distinguish ventricular tachycardia from other rhythms such as supraventricular tachycardia. The proposed algorithm is applicable on both single and dual chamber ICDs and has a low computational demand. The results demonstrate that suggested algorithm has considerable promise and merits further investigation.
Biological evaluation of biclustering algorithms using Gene Ontology and chIP-chip data

In this paper, we propose a new framework for assessing the biological significance of the outputs of any biclustering algorithm. The framework relies on the p-value computed by a Fisher's exact test on a 2x2 contingency table derived from gene ontology (GO) enrichment level and chromatin immunoprecipitation (ChIP) data enrichment level. We illustrate the framework using our published robust biclustering algorithm (RoBA), the Cheng and Church (CC) algorithm, and a well-defined set of yeast cell cycle gene expression data and chip-chip data. Our evaluation also shows that the biclusters identified by RoBA are biologically more homogeneous than the ones identified by the Cheng and Church (CC) algorithm.
DCN variations for autism diagnosis

Autism is correlated with DNA copy number variations (DCV). We used a statistical approach to analyze 75Mb of DNA samples of 71 autistic children and 71 typically developing children to prove the existence of significant differences between the two groups in their copy number burden. We applied unsupervised Fuzzy k-means clustering analysis first locally to identify the most distinctive locations, then globally to synthesize a collection of CNVs that have a strong power of segregating AU group from TD. We defined a collection of 13 CNVs with a total length ≪ 100kb that classifies 121 individuals out of 142 correctly.
Multiscale difference equation signal models - I. Theory

The paper studies multiscale difference equation models for l-D and M-D signals. In this modeling technique, the signal of interest is viewed as a solution to a multiscale difference equation (MSDE). The model completely characterizes the signal as well as a number of its higher derivatives. It provides a recursive signal interpolation scheme as a function of scale. It also leads naturally to multigrid signal filtering, detection and estimation algorithms. An MSDE model must be uniquely decodable, i.e., it must correspond to a unique signal. Therefore, one must guarantee that the modeling MSDE has a unique solution. The authors investigate the existence and uniqueness of L/sub 1/ and L/sub 2/ solutions-to multiscale difference equations. Using Fourier domain techniques, they derive conditions for the existence of L/sub 1/ solutions to an MSDE. They provide conditions under which the L/sub 1/ solution is unique (up to a multiplicative constant) and has compact support. They also derive sufficient, but not necessary, conditions for the existence of a unique L/sub 2/ solution to a subclass of MSDEs. The results extend known facts about the solutions of two-scale difference equations. The paper concludes with several examples of MSDE signal models that highlight the modeling advantages of MSDEs over two-scale difference equation models.
Monitoring of Obstructive Sleep Apnea in Heart Failure Patients

This research aims to develop a non-intrusive system to monitor obstructive sleep apnea (OSA) in heart failure patients. Heart sounds and ECG are used to develop a support vector machine (SVM) based classifier. The RMS energy in wavelet sub-bands are used as feature vectors. Feature reduction is performed to minimize complexity without loss of performance. Data from 17 patients is parsed into two minute epochs and randomly partitioned into training and test datasets. The training set is used for parameter optimization of the SVM algorithm and a test data set is used to estimate the generalization error of the algorithm. The proposed algorithm has a 85.5% sensitivity and 92.2% specificity for the detection of OSA epochs.
Detection and screening of sleep apnea using spectral and time domain analysis of heart rate variability

Sleep apnea syndrome (SAS) is one of the most common breathing related sleep disorders. Sleep apnea (SA) may be of particular concern in chronic heart failure patients due to its high levels of cardiovascular morbidity and mortality. Our aim was to assess the diagnostic potential of SA using spectral analysis of nocturnal heart rate, and to introduce new simple time domain heart rate variability (HRV) measures for screening SA. Data subjects supplied by the Physionet database (http://www.Physionet.org/challenge/2000/) were analyzed. The preliminary results established the effectiveness of the spectral algorithm as a potential detection tool for SA with p<0.0001, and demonstrated the usefulness of the new proposed time domain parameters.
Sequential energy detection for touch input detection

In this paper we propose a novel detection algorithm that delivers impressive savings in the sensing time of the capacitive touchscreen systems using sequential energy detection methods. We also show that these savings can translate into a significant boost to the operational battery life of today's mobile devices. We provide numerical analysis of sequential energy detection scheme, and the average sample number of measurements required to decide the presence of a touch input is derived. The analysis and simulation results confirm that the proposed scheme can save at least 70% or more computational resources for performing touch detection under realistic noise condition as compared to the conventional fixed sample size detection scheme.
High performance solution for interfering UWB piconets with reduced complexity sphere decoding

The sphere decoding (SD) algorithm has been widely recognized as an important algorithm to solve the maximum likelihood detection (MLD) problem, given that symbols can only be selected from a set with a finite alphabet. The complexity of the sphere decoding algorithm is much lower than the directly implemented MLD method, which needs to search through all possible candidates before making a decision. However, in high-dimensional and low signal-to-noise ratio (SNR) cases, the complexity of sphere decoding is still prohibitively high for practical applications. In this paper, a simplified SD algorithm, which combines the K-best algorithm and SD algorithm, is proposed. With carefully selected parameters, the new SD algorithm, called SD-KB algorithm, can achieve very low complexity with acceptable performance degradation compared with the traditional SD algorithm. The low complexity of the SD-KB algorithm makes it applicable to the simultaneously operating piconets (SOP) problem of the multi-band orthogonal frequency division multiplex (MB-OFDM) scheme for the high-speed wireless personal area network (WPAN). We show in particular that the proposed algorithm provides over 4 dB gain in bit error rate (BER) performance over the baseline MB-OFDM scheme when several piconets interfere with each other. The SD-KB algorithm can provide pseudo-MLD solutions, which have significant performance gain over the baseline method, especially when the signal-to-noise ratio (SNR) is low. The cost of performance improvement is a higher complexity. However, the new SD algorithm has predictable computation complexity even in the worst scenario.
Source localization techniques for direction decoding from local field potentials

Local Field Potential (LFP) recordings are one type of intracortical recordings, (besides Single Unit Activity) that can help decode movement direction successfully. In the longterm however, using LFPs for decoding presents some major challenges like inherent instability and non-stationarity. Our approach to overcome this challenge bases around the hypothesis that each task has a signature source-location pattern. The methodology involves introduction of source localization, and tracking of sources over a period of time that enables us to decode movement direction in an eight-direction center-out-reach-task. We establish that such tracking can be used for long term decoding, with preliminary results indicating consistent patterns. In fact, tracking task related source locations render up to 66% accuracy in decoding movement direction one week after the decoding model was learnt.
Multiuser diversity with quantized feedback

In this paper, we propose an optimal discrete rate switch-based multiuser diversity system (DSMUDiv) that reduces the feedback load while preserving the essential of the scheme's performance in some cases. We examine the DSMUDiv scheme using two scheduling criteria depending on the distribution of the mobile users in the cell: (i) absolute signal-to-noise ratio (SNR)-based scheduling in the case of independent and identical distributed (i.i.d.) users, and (ii) normalized SNR-based scheduling in the case of non-i.i.d. users. The paper includes the derivation of closed-form expressions of the feedback load in the case of absolute and normalized SNR-based scheduling and the spectral efficiency in the case of absolute SNR-based scheduling. Computer simulation is used to evaluate the spectral efficiency in the case of normalized SNR-based scheduling. Under slow Rayleigh fading assumption, we compare our scheme with the optimal (full feedback load) selective diversity scheme
Fast dynamic MRI using linear dynamical system model

Imaging of physiological functions using magnetic resonance imaging is limited due its slow data acquisition speed. Previously various techniques based on data sharing in the spatiotemporal k-space or sparse recovery methods have been proposed to increase imaging speeds in dynamic MRI. This paper presents a novel formulation for fast dynamic MRI which combines the generic linear dynamical system based spatiotemporal model with sparse recovery techniques. Specifically, the formulation uses a known prior time-evolution model for the physiological function implicitly and enforces the model errors (innovations) to be sparse. The preliminary results of dynamic MRI recovery experiments on an in-vivo myocardial perfusion dataset show that the proposed approach preserves details like edges and fine structures in recovered images better than previous k-space data-sharing and sparse recovery techniques individually.
Arithmetic coding with dual symbol sets and its performance analysis

In this paper, we propose a novel adaptive arithmetic coding method that uses dual symbol sets: a primary symbol set that contains all the symbols that are likely to occur in the near future and a secondary symbol set that contains all other symbols. The simplest implementation of our method assumes that symbols that have appeared in the previously are highly likely to appear in the near future. It therefore fills the primary set with symbols that have occurred in the previously. Symbols move dynamically between the two symbol sets to adapt to the local statistics of the symbol source. The proposed method works well for sources, such as images, that are characterized by large alphabets and alphabet distributions that are skewed and highly nonstationary. We analyze the performance of the proposed method and compare it to other arithmetic coding methods, both theoretically and experimentally. We show experimentally that in certain contexts, e.g., with a wavelet-based image coding scheme that has appeared in the literature, the compression performance of the proposed method is better than that of the conventional arithmetic coding method and the zero-frequency escape arithmetic coding method.
Progressive quantized projection approach to data hiding

A new image data-hiding technique is proposed. The proposed approach modifies blocks of the image after projecting them onto certain directions. By quantizing the projected blocks to even and odd values, one can represent the hidden information properly. The proposed algorithm performs the modification progressively to ensure successful data extraction without the need for the original image at the receiver side. Two techniques are also presented for correcting scaling and rotation attacks. The first approach is an exhaustive search in nature, which is based on a training sequence that is inserted as part of the hidden information. The second approach uses wavelet maxima as image semantics for rotation and scaling estimation. Both algorithms have proved to be effective in correcting rotation and scaling distortion.
Optimal information ordering in sequential detection problems with cognitive biases

In this paper sequential detection problems are treated in the context of cognitive biases. We present a general bias model and we design a generalized sequential probability ratio test (GSPRT) to mitigate the bias impact following a composite hypothesis testing approach. We also derive an optimal ordering of the incoming observations for fast detection defined in terms of the average sample number (ASN) of observations. We verify through numerical analysis that the designed detector fulfills the time and accuracy requirements. Results show that its performance emulates that of a Bayesian detector optimized for fast sequential detection in absence of biases.
Analog to digital converter resolution of multi-band OFDM and pulsed-OFDM ultra wideband systems

Multi-band OFDM ultra wideband (UWB) system is the leading proposal for the IEEE 802.15.3a standard. Multi-band pulsed-OFDM is an enhancement to this system that provides better performance with lower complexity and power consumption. In this paper we study the resolution of the analog to digital converter (ADC) that is required for both systems using the simulation results. The performance of both systems is evaluated with different ADC resolution in realistic situations using the measured indoor propagation channel models provided by the IEEE 802.15.3a standard and compared to that of a ideal infinite resolution i.e. ideal ADC. The simulation results show that in both systems four bits resolution are enough to a performance very near to the ideal case.
Focusing matrices for wideband array processing with no a priori angle estimates

A class of data transformation matrices is introduced for the estimation of angles of arrivals of multiple wideband sources. The proposed transformation matrices are unitary and minimize the average of the squared norm of the focusing error over the angles of interest without prior knowledge of source locations. They can also be used with the multigroup sources and lead to lower resolution thresholds than those obtained with other techniques. A comparison of the statistical performance of the proposed focusing procedure with that of the spatial resampling method is provided.<<ETX>>
Enhanced ultrasound imaging resolution with 3D optical patch imagery

This paper presents a method of improving 3D ultrasound image resolution by fusing the 3D ultrasound images with 3D optical imagery obtained from a limited field of view. Organ surfaces are reconstructed using fine samples from the optical image of a single side of the organ and coarse samples from ultrasound image of the hidden side. The reconstruction is based on the fact that all deformations of an organ surface lie in low dimensional structured subspaces that can be learned using training data obtained from different subjects. The multimodality fusion algorithm accounts for differences in resolution and noise levels between the different modalities. Experiment results using ex-vivo MRI, ultrasound and optical images provide the first demonstration of improving ultrasound image resolution by an average of 2.6 mm using optical images. Surface details not seen on the ultrasound image are clearly visible on the reconstructed image. Specifically, the enhanced reconstruction achieves an average error level of less than 3 mm on the hidden organ surface.
Audio coding for conversion to MIDI

We concentrate on the problem of converting audio samples into MIDI data, and describe a technique for processing music signals that attempts to extract note onsets and pitches, for a small class of music signals. Segmentation of the signals is based upon common onsets of tones and their proximity to edges in the signal. Pitch detection is performed by decomposing the short-time spectra of the segments as linear combinations of spectra in a dictionary of recorded sound segments.
Low-complexity receiver structure with sub-Nyquist analog to digital sampling rate for pulsed-OFDM ultra wideband communication systems

Pulsed orthogonal frequency division multiplexing (pulsed-OFDM) is a simple frequency spreading technique that has been proposed to design low-complexity high performance multi-carrier ultra wideband (UWB) communication systems. A typical receiver for this system samples the received signal at Nyquist rate capturing all diversity branches. In this paper, we propose an alternate complexity-reduced receiver structure that uses analog to digital converters (ADC) with the sampling rate equal to a fraction of the Nyquist rate. We show that the output of the ADC in this case is a subset of the original diversity branches and contains complete information for all sub-carriers. Simulation results show that this technique outperforms the receiver with full rate sampling and selection diversity combining with considerable reduction in the complexity and power consumption. We also examine the possibility of enhancing the performance of the receiver by applying a subset selection algorithm based on the training sequences at the beginning of the every data packet. Simulation results in Rayleigh fading channel shows that the enhancement is negligible and we can adopt reduced-complexity receiver without selection algorithm.
N-tone sigma-delta UWB-OFDM transmitter and receiver

A new method for generating and detecting the UWB-OFDM signal using a modified sigma-delta modulator is proposed. Unlike narrowband OFDM, the UWB-OFDM spectrum can have gaps between subcarriers. The modified sigma-delta modulator, dubbed N-Tone sigma-delta, introduces N zeros at the frequencies in the quantization noise spectrum. These zeros match the locations of frequencies used by the OFDM system and the quantization noise spectrum fills the gaps in the spectrum of the UWB-OFDM signal. In fact this new structure could be used in other UWB systems anytime we have gaps in the spectrum of the transmitted signal. We describe both the transmitter and receiver structures for UWB-OFDM. We also study the spectrum of the underlying system.
A Turbo Coding Scheme for Channels with Synchronization Errors

We describe a turbo coding scheme with nonsystematic recursive convolutional codes for channels that exhibit insertions and deletions along with substitution errors. The scheme uses the MAP decoding algorithm for this channel and a special codeword arrangement to simplify the synchronization process. The decoder uses a special trellis structure to extract synchronization extrinsic information that is exchanged between the two constituent decoders. The proposed scheme is shown to provide improvement over earlier channel coding approaches for this channel model.