nils-herrmann/clean_scirepeval huggingface dataset:
Pulsed OFDM modulation for ultra wideband communications

We describe a new orthogonal frequency division multiplexing (OFDM) scheme, named pulsed-OFDM, for ultra wideband (UWB) communications. Pulsed-OFDM modulation uses pulsed sinusoids instead of continuous sinusoids to send information in parallel over different sub-carriers. Pulsating OFDM symbols spreads the spectrum of the modulated signals in the frequency domain leading to a spreading gain equal to the inverse of the duty cycle of the pulsed sub-carriers. The spreading gain provided by this system leads to an enhanced performance in multipath fading environments. We also show that at the receiver part of the multipath diversity can be exploited by pulsed-OFDM system leading to a low complexity and low power consumption transceiver structure. Easy implementation and frequency spreading of pulsed-OFDM modulation make it a proper modulation for UWB communications where the ratio of bandwidth to the data rate is large but power spectral density is limited. We design a low complexity and low power consumption pulsed-OFDM system for the IEEE 802.15.3a wireless personal area networks and compare it with the normal OFDM system has been proposed for this standard. We also provide realistic simulation results for the measured indoor propagation channels provided by the IEEE 802.15.3a standard to demonstrate the advantages of the pulsed OFDM modulation for UWB communications.
Implementation of a directional beacon-based position location algorithm in a signal processing framework

We present the implementation of a directional beacon-based positioning algorithm using radio frequency signals. This algorithm allows each mobile node to compute its position with respect to a set of reference nodes which are equipped with a rotating directional antenna. The use of directional beacon-based algorithm for position location eliminates the need for strict synchronization between the reference nodes and the mobile node. In contrast to positioning algorithms that rely on signal propagation time and bandwidth, the proposed algorithm depends on the beam-width and rotational speed of the directional antenna. We will show that these parameters can be optimized with a low cost solution that provides good positioning accuracy. The system implementation is based on the GNU radio software platform and the universal software radio peripheral as the hardware component. We present an enhanced maximum likelihood method for estimating the received signal amplitude profile. To deal with obstructed line-of-sight scenarios, we do not rely purely on the received signal strength and instead formulate a least squares problem to estimate the line-of-sight component in a multipath environment. These advanced signal processing techniques yield a more accurate estimate of the bearing of the mobile node with respect to each of the reference nodes. We also show that the proposed positioning algorithm is tolerant to errors in timing and synchronization. We demonstrate the ability to obtain mobile node position estimates with sub-meter accuracy by transmitting a narrowband signal of 1 kHz bandwidth in the 2.4-2.5 GHz band. The experimental results show a mean position error of 0.759 m, in a field measuring 55m by 43m, using eight 90° rotations of the antenna.
Biclustering of DNA microarray data with early pruning

Uncovering genetic pathways is equivalent to finding clusters of genes with expression levels that evolve coherently under subsets of conditions. This can be done by applying a biclustering procedure to gene expression data. We propose a new biclustering procedure that derives biclusters from candidate subsets of conditions. These candidate subsets of conditions are identified by comparing pairs of gene expression data. To reduce complexity, the procedure discards early in the candidate subset of conditions formation stage any subset that is predicted to have less than a desired minimum number of conditions. When the biclusters are required to have more than a minimum number of genes, we show that further reduction in complexity can be achieved with no loss of performance by comparing each gene with only a subset of all genes. The proposed approach finds all genes expression levels that evolve coherently under each of the candidate subsets of conditions using a fast approximate pattern matching technique. This approximate pattern matching procedure can find a pattern in a list even if instances of the pattern in the list have random insertions of characters between consecutive characters in the pattern. As compared to prior techniques, the approach finds all maximum size biclusters with a number of conditions greater than a specified minimum. It has a run time equivalent to the fastest of these techniques, even though the fastest biclustering techniques are not guaranteed to find all biclusters.
Optimal sensor location for body sensor network to detect self-stimulatory behaviors of children with autism spectrum disorder

In this study, we investigate various locations of sensor positions to detect stereotypical self-stimulatory behavioral patterns of children with Autism Spectrum Disorder (ASD). The study is focused on finding optimal detection performance based on sensor location and number of sensors. To perform this study, we developed a wearable sensor system that uses a 3 axis accelerometer. A microphone was used to understand the surrounding environment and video provided ground truth for analysis. The recordings were done on 2 children diagnosed with ASD who showed repeated self-stimulatory behaviors that involve part of the body such as flapping arms, body rocking and vocalization of non-word sounds. We used time-frequency methods to extract features and sparse signal representation methods to design over-complete dictionary for data analysis, detection and classification of these ASD behavioral events. We show that using single sensor on the back achieves 95.5% classification rate for rocking and 80.5% for flapping. In contrast, flapping events can be recognized with 86.5% accuracy using wrist worn sensors.
Real Time 3D Visualization of Intraoperative Organ Deformations Using Structured Dictionary

Restricted visualization of the surgical field is one of the most critical challenges for minimally invasive surgery (MIS). Current intraoperative visualization systems are promising. However, they can hardly meet the requirements of high resolution and real time 3D visualization of the surgical scene to support the recognition of anatomic structures for safe MIS procedures. In this paper, we present a new approach for real time 3D visualization of organ deformations based on optical imaging patches with limited field-of-view and a single preoperative scan of magnetic resonance imaging (MRI) or computed tomography (CT). The idea for reconstruction is motivated by our empirical observation that the spherical harmonic coefficients corresponding to distorted surfaces of a given organ lie in lower dimensional subspaces in a structured dictionary that can be learned from a set of representative training surfaces. We provide both theoretical and practical designs for achieving these goals. Specifically, we discuss details about the selection of limited optical views and the registration of partial optical images with a single preoperative MRI/CT scan. The design proposed in this paper is evaluated with both finite element modeling data and ex vivo experiments. The ex vivo test is conducted on fresh porcine kidneys using 3D MRI scans with 1.2 mm resolution and a portable laser scanner with an accuracy of 0.13 mm. Results show that the proposed method achieves a sub-3 mm spatial resolution in terms of Hausdorff distance when using only one preoperative MRI scan and the optical patch from the single-sided view of the kidney. The reconstruction frame rate is between 10 frames/s and 39 frames/s depending on the complexity of the test model.
Rhythm and periodicity detection in polyphonic music

We describe a novel approach for detecting perfect and imperfect periodicities in polyphonic music. The approach relies on beat and rhythm information extracted from the raw data after low-pass filtering. The beat and rhythm information is analyzed with a binary tree or trellis tree parsing depending on the length of the pauses in the underlying signal. This analysis yields accurate periodicity patterns at macro and micro scales. We illustrate the effectiveness of our approach using music segments from various cultures and explain its use in music classification and content-based retrieval.
Acoustic emission classification using signal subspace projections

In using acoustic emissions (AE) for mechanical diagnostics, one major problem is the differentiation of events due to crack growth in a component from noise of various origins. This work presents two algorithms for automatic clustering and separation of AE events based on multiple features extracted from experimental data. The first algorithm consists of two steps. In the first step, the noise is separated from the events of interest and subsequently removed using a combination of covariance analysis, principal component analysis (PCA), and differential time delay estimates. The second step processes the remaining data using a self-organizing map (SOM), which outputs the noise and AE signals into separate neurons. The algorithm is verified with two sets of data, and a correct classification ratio of over 95% is achieved. The second algorithm characterizes the AE signal subspace based on the principal eigenvectors of the covariance matrix of an ensemble of the AE signals. The latter algorithm has a correct classification ratio over 90%.
Efficient design of ultrasound true-velocity flow mapping

The authors describe the general theory of the classical Doppler technique for flow mapping and show that its main assumptions do not generally hold for ultrasound imaging. They then develop a generalized model for frequency-domain flow mapping in practical ultrasound imaging situations. Using this model, the authors show that it is possible to compute the true velocity from single-aperture configurations. They discuss improving the resolution and velocity estimation accuracies and propose a novel approach based on a generalization of the radar ambiguity function model. They also consider the same problems for time-domain techniques. They propose a generalization of the correlation technique that takes into account the ultrasound field effect, and show that it is theoretically possible to obtain true-velocity flow maps from single-aperture configurations. Finally, the authors discuss the relative advantages and disadvantages of both frequency-domain and time-domain techniques.
QRP07-5: Cross-layer-based Modeling of IEEE 802.11 Wireless LANs with MIMO Links

Multiple-input multiple-output (MIMO) is the new technology that has the potential of increasing the transmission speeds and extending the range in wireless LANs. While MIMO links has been extensively researched in the PHY layer research community, very few studies has been done on the MAC level. In this paper we perform cross-layer analysis to study the impact of spatial multiplexing using MMSE and ZF on the MAC layer. We discuss an analytical model that takes into account the packet error rate (PER) as a loss factor in calculating the MAC throughput. The model is built on the legacy IEEE 802.11 DCF access mechanism. Expressions for throughput and average service time of packets are provided. The analytical expressions are solved using MATLAB and the results are validated by simulations.
Low power detection using stochastic resonance

Mobile communications dictates the use of low power detection and estimation algorithms to prolong the battery life. We present a very low power detection scheme and evaluate its performance. This scheme can be used instead of the optimal quantized detectors when the noise variance, or other problem parameters, are unknown or change online. This detector uses an adaptive discrete time stochastic resonator that consists of a simple Schmitt trigger. Theoretical results evaluating the detection capability of such a device are presented, and simulations show an improvement in the detection probability over other low power schemes.
Multiresolution scene-based video watermarking using perceptual models

We present a watermarking procedure to embed copyright protection into digital video. Our watermarking procedure is scene-based and video dependent. It directly exploits spatial masking, frequency masking, and temporal properties to embed an invisible and robust watermark. The watermark consists of static and dynamic temporal components that are generated from a temporal wavelet transform of the video scenes. The resulting wavelet coefficient frames are modified by a perceptually shaped pseudorandom sequence representing the author. The noise-like watermark is statistically undetectable to thwart unauthorized removal. Furthermore, the author representation resolves the deadlock problem. The multiresolution watermark may be detected on single frames without knowledge of the location of the frames in the video scene. We demonstrate the robustness of the watermarking procedure to several video degradations and distortions.
Multi-Channel Sparse Data Conversion With a Single Analog-to-Digital Converter

We address the problem of performing simultaneous analog-to-digital (A/D) conversion on multi-channel signals using a single A/D converter (ADC). Assuming that each input has an unknown sparse representation in known dictionaries, we find that multi-channel information can be sampled with a single ADC. The proposed ADC architecture consists of a mixed signal block and a digital signal processing (DSP) block. The channel inputs are sampled by switched-capacitor-based sample-and-hold circuits, and then mixed using sequences of plus or minus ones, leading to no bandwidth expansion. The resulting discrete-time signals are converted to digital sequences by a single ADC or quantizer. At the DSP block, each channel is separated from the digitized mixture through various separation algorithms that are widely used in compressive sensing. For this, we study several techniques for separating the mixture of the channel inputs into the sample number of digital sequences corresponding to each channel. We show that with an ideal ADC, perfect reconstruction of the signals is possible if the input signals are sufficiently sparse. We also show simulation results with a 16-bit ADC model, and the reconstruction is possible up to the accuracy of the ADCs.
Optimal information sequencing for cognitive bias mitigation

Decades of research indicate that humans are not rational decision-makers. Our decisions and assessments of situations we encounter and other individuals or groups are sometimes flawed because they are based on a limited acquisition and rational analysis of information, and strongly influenced by our past experiences. We develop in this paper mathematical models of human decision-making that incorporate the effect of cognitive biases. These models start from an optimal Bayesian decision making algorithm and modify it to account for cognitive biases and the effect of past information seen by the individual. Next, we show how it is possible to mitigate cognitive biases in binary hypothesis testing problems by properly selecting and sequencing information presented to an individual.
Estimation of range-Doppler radar images

It is shown that 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, a solution is discussed for the problem of choosing a set of N waveforms to reconstruct with high accuracy an arbitrary unknown target range-Doppler density function.<<ETX>>
Media compression via data hiding

Data hiding procedures embed digital data in a host audio, image or video signal. The embedded data is perceptually and statistically invisible until extracted by the proper algorithm. In this paper we investigate the use of data hiding for media compression. We propose a media compression scheme which applies data hiding procedures to "fold" a media signal into itself. Specifically the media signal to be compressed is split into host and residual parts. The residual part is compressed and embedded into the host part. The host part is compressed with standard compression techniques and transmitted to the decoder. As a result, only part of the original signal is needed to be compressed, and the perceptual quality of the compressed signal is not sacrificed. Bit rate reduction by the proposed method is proportional to the redundancy and irrelevancy remaining in the compressed signal. The proposed scheme works since no current compression schemes can completely remove the redundancy and irrelevancy in a signal.
Multiuser diversity-enhanced equal access with quantized feedback in multicarrier OFDM systems

Orthogonal frequency division multiplexing (OFDM) is emerging as a key modulation scheme for future high speed packets based wireless transmission systems. In a wireless multiuser network system, when an opportunistic scheduling is employed in the OFDM system with adaptive modulation, it may encounter implementation problems due to the need for a large amount of channel information. In this paper, we adopt an idea that we proposed in (1) called discrete rate switch-based multi-user diversity system (DSMUDiv) and combine it with the idea of Enhanced Equal Access scheduling policy that was proposed in (2) to reduces the feedback load and guarantee high probability of access for each user while maintaining most of the performance of opportunistic scheduling. We present an analytical analysis of the feedback load, spectral efficiency and probability of access of the proposed scheme.
A Space-time-Frequency Analysis Approach for the Classification Motor Imagery EEG Recordings in a Brain Computer Interface Task

We introduce an adaptive space time frequency analysis to extract and classify subject specific brain oscillations induced by motor imagery in a brain computer interface task. The introduced method requires no prior knowledge of the reactive frequency bands, their temporal behavior or cortical locations. The algorithm implements an arbitrary time-frequency segmentation procedure by using a flexible local discriminant base algorithm for given multichannel brain activity recordings to extract subject specific ERD and ERS patterns. Extracted time-frequency features are processed by principal component analysis to reduce the feature set which is highly correlated due to volume conduction and the neighbor cortical regions. The reduced feature set is then fed to a linear discriminant analysis for classification. We give experimental results for 9 subjects to show the superior performance of the proposed method where the classification accuracy varied between 76.4% and 96.8% and the average classification accuracy was 84.9%
Transparent robust authentication and distortion measurement technique for images

We propose a novel scheme to embed an invisible signature into an image to check image integrity and measure its distortion. The technique is based on the pseudo-noise sequences and visual masking effects. The values of an image are modified by a pseudo-noise signature which is shaped by the perceptual thresholds from masking effects. The method is robust and can gauge errors accurately up to half of the perceptual thresholds. It also readily identifies large image distortion. Experimental results after applying JPEG and white noise to the image are also reported.
Efficient Updating of Biological Sequence Analyses

We present a novel approach for reducing the computational complexity of updating homologies produced by a wide class of popular state-of-the-art algorithms in comparative computational biology. The algorithms that we consider use hidden Markov models (HMMs) and a Viterbi recursion to evaluate matches between sequences, or between a sequence and models. Such updates occur frequently in practice as researchers discover errors in biological sequences or analyze multiple nearly similar sequences, e.g., in a family of proteins that underwent mutations during evolution. The proposed algorithm interprets the Viterbi recursion as an update of an optimal minimum spanning tree in a shortest path problem. We propose the novel concept of a relative node tolerance bound and show how it can be used to guarantee that one or more partial subtrees of a minimum spanning tree obtained before encountering the perturbations remain optimal. We also describe how to compute and use in real-time the relative node tolerance bounds to skip most unperturbed parts of a sequence while computing the new optimal solution. To further reduce the computational overhead associated with the tolerance bound evaluation, we present and exploit a statistical analysis of the matching procedure that estimates how many columns in the dynamic program that corresponds to the matching problem are affected by a change in a preceding column. The resulting "reusable" Viterbi decoding algorithm can update a matching result in less than a third to a fifth of the time required to compute a new match by performing a normal matching procedure, i.e., running a Viterbi algorithm with updated sequences against a base hidden Markov model.
Wavelet decomposition of binary finite images

Constructs a theory of wavelet decompositions of binary images. The construction defines binary valued wavelets and scaling functions and their associated spectral properties. The authors begin by introducing a new binary field transform and the corresponding concept of sequence spectra over GF(2). Using this transform, a theory of binary wavelets is then developed in terms of 2-band perfect reconstruction filter banks. By generalizing the corresponding real field constraints of bandwidth, vanishing moments, and spectral content in the filters, a perfect reconstruction wavelet decomposition is created. An example to illustrate the potential use for compression applications is included.<<ETX>>
Inference using phi-divergence Goodness-of-Fit tests

In this paper we study the inferential use of goodness of fit tests in a non-parametric setting. The utility of such tests will be demonstrated for the test case of spectrum sensing applications in cognitive radios. For the first time, we provide a comprehensive framework for decision fusion of a ensemble of goodness-of-fit testing procedures through an Ensemble Goodness-of-Fit test. Also, we introduce a generalized family of functionals and kernels called Φ-divergences which allow us to formulate goodness-of-fit tests that are parameterized by a single parameter s. The performance of these tests is simulated under gaussian and non-gaussian noise in a MIMO setting. We show that under uncertainty or non-gaussianity in the noise, the performance of non-parametric tests in general, and phi-divergence based goodness-of-fit tests in particular, is significantly superior to that of the energy detector with reduced implementation complexity. Especially important is the property that the false alarm rates of our proposed tests is maintained at a fixed level over a wide variation in the channel noise distributions.