nils-herrmann/clean_scirepeval huggingface dataset:
Designing commutative cascades of multidimensional upsamplers and downsamplers

In multiple dimensions, the cascade of an upsampler by L and a downsampler by M commutes if and only if the integer matrices L and M are right coprime and LM=ML. This letter presents algorithms to design L and M that yield commutative upsampler/dowsampler cascades. We prove that commutativity is possible if the Jordan canonical form of the rational (resampling) matrix R=LM/sup -1/ is equivalent to the Smith-McMillan form of R. A necessary condition for this equivalence is that R has an eigendecomposition and the eigenvalues are rational.
A real-time embedded software implementation of a turbo encoder and soft output Viterbi algorithm based turbo decoder

Turbo codes are used for error protection, especially in wireless systems. A turbo encoder consists of two recursive systematic convolutional component encoders connected in parallel and separated by a random interleaver. A turbo decoder, which is iterative, is typically based on either a soft output Viterbi algorithm (SOVA) or a maximum a posteri (MAP) algorithm. MAP is roughly three times more computationally complex than SOVA, but provides 0.5 dB of coding gain. We implement a turbo encoder and SOVA-based turbo decoder in real-time software on a TMS320C6700 digital signal processor (DSP). The contributions of this paper are: (1) first publicly available implementation of a SOVA-based turbo decoder on a C6000 DSP (2) speedup of 162x for the encoder on a C6200 DSP and 11.7x for the decoder on a C6700 DSP over level three C compiler optimization, and (3) dataflow modeling for a turbo channel coding subsystem.
Imposing structure on Smith-form decompositions of rational resampling matrices

Imposing structure on the Smith form of an (integer) periodicity matrix N=U/spl Lambda/V leads to efficient multidimensional (m-D) DFT implementations (Guessoum, 1984). For nonsingular integer and rational matrices, the authors introduce algorithms to generate U (or V) matrices with minimum norm and /spl Lambda/ matrices whose diagonal elements exhibit minimum variance. Such structure simplifies nonuniform m-D filter-bank design (Gardos et al. 1992).
Optimal power allocation in multiuser OFDM systems

Multiuser orthogonal frequency division multiplexing (MU-OFDM) is a promising technique for achieving high downlink capacities in future cellular systems. A key issue in MU-OFDM is the allocation of the OFDM subcarriers and power among users sharing the channel. Previous allocation algorithms cannot ensure fairness in advance. In this paper, a proportional rate adaptive resource allocation method for MU-OFDM is proposed. Subcarrier and power allocation are carried out sequentially to reduce the complexity, and an optimal power allocation procedure is derived, through which proportional fairness is achieved. Simulation results show that this low-complexity MU-OFDM system achieves double the capacity of a fixed time division approach to OFDM multiple access, and also has higher capacity than previously derived suboptimal power distribution schemes.
Improved matrix pencil methods

We study the problem of estimating signal parameters from a noisy data sequence containing superimposed damped sinusoids. We propose three novel methods by combining the reduced-rank Hankel approximation and the matrix pencil method. We demonstrate that two of the proposed methods significantly outperform both the original matrix pencil method and the modified Kumaresan-Tufts (1982) method, especially at low signal-to-noise ratio.
Infinite length results and design implications for time-domain equalizers

We show that maximum shortening signal-to-noise ratio (SNR) time-domain equalizers (TEQs) are often nearly symmetric. Constraining the TEQ to be symmetric causes only a 3% loss in bit rate (averaged over eight standard ADSL channels). Symmetric TEQs have greatly reduced design and implementation complexity. We also show that for infinite length TEQs, minimum mean squared error (MMSE) target impulse responses have all zeros on the unit circle, which can lead to poor bit rate performance.
Visual attention guided quality assessment of Tone-Mapped images using scene statistics

Measuring visual quality, as perceived by human observers, is becoming increasingly important in the many applications in which humans are the ultimate consumers of visual information. This paper assesses the visual quality in mapping of high dynamic range (HDR) images to standard dynamic range (SDR) images with 8 bits/color/pixel. In previous work, the Tone-Mapped image Quality Index (TMQI) compares the original HDR image with the rendered SDR image. TMQI quantifies distortions locally and pools them by uniform averaging, in addition to measuring naturalness of the SDR image. For SDR images, perceptual pooling strategies have improved correlation of image quality assessment (IQA) algorithms with subjective scores. The primary contributions of this paper are: (1) integrating local information-based pooling strategies in the TMQI IQA algorithm, (2) measuring image naturalness by using mean-subtracted contrast-normalized pixels, and (3) testing the proposed methods on JPEG compressed tone-mapped images and tone-mapped images for SDR displays using subjective scores.
Cochannel signal separation in fading channels using a modified constant modulus array

We convert a constant modulus (CM) array into a robust smart antenna by modifying the error criterion to be a weighted sum of conventional CM array error and decision-directed equalization error. The new error criterion enables the CM array to (1) separate digital cochannel signals with multipath and inter-symbol interference and (2) track fading signals. The key contribution is that the modified error criterion adds phase sensitivity to the otherwise phase insensitive CM error criterion. We present computer simulations to show the signal tracking properties of the CM array using the modified error criterion in a fading environment.
Machine Learning Approach to Estimating mmWave Signal Measurements During Handover

We propose to estimate mmWave received signal power levels using supervised machine learning and LTE signal measurements in sub-6 GHz bands. Contributions of this paper include 1) introduction of partially blind handovers, 2) demonstration that LTE measurements from sub-6 GHz LTE bands can help improve handover to mmWave frequency bands, and 3) an algorithm to improve handover success rates using an extreme gradient boosting classifier. Simulation results show an improvement of 1.5% in the handover success rate by the proposed method over the baseline method.
Blind estimation of FIR channels in CDMA systems with aperiodic spreading sequences

CDMA systems commonly use aperiodic spreading codes to distribute a signal spectrum uniformly over the channel bandwidth and differentiate neighboring cell sites. CDMA receivers often suffer from interference due to multipath fading. Blind signal estimation schemes cannot be used because they require periodic spreading sequences. RAKE receivers are often used, but they cannot fully exploit the rich structure of CDMA signals to minimize interference. This paper presents an iterative technique to estimate multipath parameters which can serve as a preprocessing step in a receiver to increase signal-to-interference ratio. We investigate the performance of the proposed method using computer simulations. Preliminary simulation results show an average of 10 dB gain on channel parameter estimation.
A factor analytic approach to inferring congestion sharing based on flow level measurements

Internet traffic primarily consists of packets from elastic flows, i.e., Web transfers, file transfers, and e-mail, whose transmissions are mediated via the Transmission Control Protocol (TCP). In this paper, we develop a methodology to process TCP flow measurements in order to analyze throughput correlations among TCP flow classes that can be used to infer congestion sharing in the Internet. The primary contributions of this paper are: 1) development of a technique for processing flow records suitable for inferring congested resource sharing; 2) evaluation of the use of factor analysis on processed flow records to explore which TCP flow classes might share congested resources; and 3) validation of our inference methodology using bootstrap methods and nonintrusive, flow level measurements collected at a single network site. Our proposal for using flow level measurements to infer congestion sharing differs significantly from previous research that has employed packet level measurements for making inferences. Possible applications of our method include network monitoring and root cause analysis of poor performance.
Sensitivity analysis of a spatially-adaptive estimator for data fusion

We analyze the parametric sensitivity of a spatially-adaptive multiscale data fusion method. The fusion problem is formulated as a recursive estimation problem in scale and space using a set of 1D Kalman filters. The overall filter accommodates data acquired at different resolutions and missing data. The filter approaches optimal performance for data with spatially-varying statistics by adaptively updating the filter parameters using the innovation-correlation method. The contribution of this paper is the determination of the estimation error sensitivity to the process noise and measurement noise variances.
Statistics of Co-Channel Interference in a Field of Poisson and Poisson-Poisson Clustered Interferers

With increasing spatial reuse of radio spectrum, co-channel interference is becoming a dominant noise source and may severely degrade the communication performance of wireless transceivers. In this paper, we consider the problem of statistical-physical modeling of co-channel interference from an annular field of Poisson or Poisson-Poisson cluster distributed interferers. Poisson and Poisson-Poisson cluster processes are commonly used to model interferer distributions in large wireless networks without and with interferer clustering, respectively. Further, by considering the interferers distributed over a parametric annular region, we derive interference statistics for finite- and infinite- area interference region with and without a guard zone around the receiver. Statistical modeling of interference is a useful tool to analyze outage probabilities in wireless networks and design interference-aware transceivers. Our contributions include: 1) developing a unified framework for deriving interference models for various wireless network environments; 2) demonstrating the applicability of the symmetric alpha stable and Gaussian mixture (with Middleton Class A as a particular form) distributions in modeling co-channel interference; and 3) deriving analytical conditions on the system model parameters for which these distributions accurately model the statistical properties of the interference. Applications include co-channel interference modeling for various wireless networks, including wireless ad hoc, cellular, local area, and femtocell networks.
A probabilistic framework for Global Navigation Satellite System signal timing assurance

Global Navigation Satellite System (GNSS) signals serve as a worldwide timing reference in numerous technological sectors. Yet GNSS receivers are vulnerable to so-called spoofing attacks that can manipulate the time reference. We illustrate the need for a probabilistic security model in the context of authenticating a timing signal as opposed to the traditionally non-probabilistic security models of message authentication and cryptography. Our primary contribution is establishing the necessary conditions for timing assurance in the context of security-enhanced GNSS signals. In addition, we formulate a probabilistic framework for timing assurance that combines cryptography and statistical signal processing across multiple network layers.
Alleviating Dirty-Window Effect in Medium Frame-Rate Binary Video Halftones

A video display device having a lower number of bits per pixel than that required by the video to be displayed quantizes the video prior to its display. Halftoning can perform this quantization while attempting to reduce the visibility of certain quantization artifacts. Quantization artifacts are, nevertheless, not eliminated. A temporal artifact known as dirty-window effect (DWE) can be commonly observed in medium frame-rate binary video halftones. In this paper, we propose video halftone enhancement algorithms to reduce DWE. We assess the performance of the proposed algorithms by presenting objective measures for DWE in the original and the improved halftone videos. The expected contributions of this paper include three medium frame-rate binary video halftone enhancement algorithms that do the following: 1) reduce DWE under a spatial quality constraint; 2) reduce DWE under a spatial quality constraint with reduced complexity; and 3) reduce DWE under spatial and temporal quality constraints.
Joint Temporal Statistics of Interference in Decentralized Wireless Networks

Characterizing interference statistics is central to the design and analysis of both physical layer and medium access control layer techniques to mitigate interference in a wireless network. The applicability of interference statistics, however, is limited by the assumptions adopted to derive the statistics in closed-form. Common assumptions for a decentralized wireless network include temporally independent user locations and an unbounded pathloss function. In this correspondence, we derive the joint temporal statistics of interference that capture the temporal correlation in the network along with the realistic assumption of a bounded pathloss function. The closed-form statistics are asymptotically exact for low tail probabilities, and match closely in simulations even when the tail probability is fairly high. The primary contributions are to i) show that joint interference statistics follow a multivariate Gaussian mixture distribution under the assumption of a bounded pathloss function, and ii) characterize the joint tail probability decay behavior for both bounded and unbounded pathloss functions.
Perceptual Image Hashing Via Feature Points: Performance Evaluation and Tradeoffs

We propose an image hashing paradigm using visually significant feature points. The feature points should be largely invariant under perceptually insignificant distortions. To satisfy this, we propose an iterative feature detector to extract significant geometry preserving feature points. We apply probabilistic quantization on the derived features to introduce randomness, which, in turn, reduces vulnerability to adversarial attacks. The proposed hash algorithm withstands standard benchmark (e.g., Stirmark) attacks, including compression, geometric distortions of scaling and small-angle rotation, and common signal-processing operations. Content changing (malicious) manipulations of image data are also accurately detected. Detailed statistical analysis in the form of receiver operating characteristic (ROC) curves is presented and reveals the success of the proposed scheme in achieving perceptual robustness while avoiding misclassification
Color error-diffusion halftoning

Grayscale halftoning converts a continuous-tone image (e.g., 8 bits per pixel) to a lower resolution (e.g., 1 bit per pixel) for printing or display. Grayscale halftoning by error diffusion uses feedback to shape the quantization noise into high frequencies where the human visual system (HVS) is least sensitive. In color halftoning, the application of grayscale error-diffusion methods to the individual colorant planes fails to exploit the HVS response to color noise. Ideally the quantization error must be diffused to frequencies and colors, to which the HVS is least sensitive. Further it is desirable for the color quantization to take place in a perceptual space so that the colorant vector selected as the output color is perceptually closest to the color vector being quantized. This article discusses the design principles of color error diffusion that differentiate it from grayscale error diffusion, focusing on color error diffusion halftoning systems using the red, green, and blue (RGB) space for convenience.
Optimal resource allocation in the OFDMA downlink with imperfect channel knowledge

Previous research efforts on OFDMA resource allocation have typically assumed the availability of perfect channel state information (CSI). Unfortunately, this is unrealistic, primarily due to channel estimation errors, and more importantly, channel feedback delay. In this paper, we develop optimal resource allocation algorithms for the OFDMA downlink assuming the availability of only partial (imperfect) CSI. We consider both continuous and discrete ergodic weighted sum rate maximization subject to total power constraints, and average bit error rate constraints for the discrete rate case. We approach these problems using a dual optimization framework, allowing us to solve these problems with O(MK) complexity per symbol for an OFDMA system with K used subcarriers and M active users, while achieving relative optimality gaps of less than 10-5 for continuous rates and less than 10-3 for discrete rates in simulations based on realistic parameters.
Space-time fronthaul compression of complex baseband uplink LTE signals

In this paper, we propose space-time fronthaul compression of baseband uplink LTE signals for cellular networks, in which baseband units (BBUs) support remote radio heads (RRHs) through fronthaul links. In particular, we assume massive antenna arrays in which the number of antennas in a RRH is much larger than the number of active users. The proposed method consists of two phases: dimensionality reduction phase and individual quantization phase. The key idea of the first phase is to apply principal component analysis (PCA). It performs low-rank approximation of a matrix - composed of received signals - by exploiting the correlation of the received signals across space and time. In the second phase, our method individually quantizes the dimensionality-reduced signal by applying transform coding with bit allocation to reduce the number of quantization bits. An LTE link-level simulator provides numerical results which show that the method achieves up to 8 × compression ratio for the uplink with 64 antennas and 4 active users, along with improvement in communication system performance as a result of denoising.
Modeling and design of portable Compton gamma-ray cameras

Compton cameras have certain applications requiring portability. Camera designs which improve the portability of these devices by using room-temperature semiconductor and position-sensitive scintillation detectors have been investigated. Models have been developed and tested for the angular image resolution and energy resolution of the camera. A critical component of the models is a Monte Carlo code for simulating the light collection in the position-sensitive scintillator. Model testing has been accomplished with measurements made using a simplified camera having only one room-temperature Si(Li) detector and one position-sensitive NaI(Tl) detector. The model accurately matches measured resolutions over the energy range from 0.66 to 2.75 MeV. The efficiency and time resolution of the portable camera designs have also been investigated. The models predict the angular image resolution for a camera based on room-temperature Si(Li) and NaI(Tl) detectors to be between 9 and 5 degrees for incident photons from 0.5 to 3.0 MeV. The resolution of a camera with a CsI(Tl) detector in place of the NaI(Tl) is predicted to improve the resolution to between 7 and 4 degrees in this energy range.
Real-time 3D rotation smoothing for video stabilization

We propose two real-time motion smoothing algorithms for video stabilization using a pure 3D rotation motion model with known camera projection parameters. Both proposed algorithms aim at smoothing 3D rotation matrix sequences in a causal way. The first algorithm smooths the 3D rotation sequences in a way similar to 1st-order IIR filtering. The second algorithm uses sequential probabilistic estimation under a constant angular velocity model. These two algorithms are generalized from classical 2D motion smoothing algorithms. We exploit the manifold structure of the rotation matrices so that the proposed algorithms directly smooth the 3D rotation sequences on the manifold. In addition, we introduce a simple projection step in order to guarantee that no black borders intrude into the stabilized video frames. Experimental results show that the proposed algorithms are able to effectively stabilize video sequences and outperform their 2D counterparts with less jitter and distortion.
System modeling and implementation of a generic video codec

The rapidly emerging and increasing complexity of video coding standards require a new design paradigm. This paper describes a modular, scalable, extensible simulation and design methodology for system-level design of video codecs. Video codec dataflow is modeled by synchronous dataflow, and implemented in a heterogeneous CAD framework. As a result, generic video codec is decomposed into basic modules. Each module is easy to interface and extend. Any video codec standard (e.g., H.263+ or MPEG-4) can be mapped on that basis and be retargeted for various architectures including DSPs and ASIPs. We develop module libraries for video codecs which can be dynamically linked to an extensible framework for simulation and algorithm development. Some parts of the basic modules can be mixed with other domain modules which may have different interaction semantics especially for hardware design and retargeting purposes. We based our framework on the Ptolemy software environment.
Joint channel estimation and prediction for OFDM systems

Adaptive OFDM improves the system throughput by adjusting transmission parameters based on channel state information (CSI) estimated at or received by the transmitter. The improvement, however, is conditioned on the quality of the CSI, which can be compromised by estimation and quantization errors, and more significantly by delay. Channel prediction has been previously proposed to combat feedback delay. In this paper, a novel OFDM channel prediction algorithm that uses a 2 times 1-dimensional frequency estimation to determine the time-delays and Doppler frequencies of each propagation path is investigated. The algorithm assumes a general far-field scatterer, frequency-selective wireless channel model and hence is applicable to a wide variety of wireless channel conditions. It is shown that the method requires less feedback information and has better mean-squared error performance than previous methods. We provide simulation results for IEEE 802.16e channels
Improved accuracy for interferometric radar images using polarimetric radar and laser altimetry data

The ability to measure land surface topography over large areas to assess natural hazard threats posed by seismic and flooding events is a critical international need. Interferometric synthetic aperture radar (INSAR) has been used to map topography; however, accuracies are limited because observations are not measurements of true surface topography over vegetated areas. Instead, the measurements, which depend on the sensor and the vegetation, represent some height above the true surface. We develop a two-step correction for the INSAR imagery to account for penetration into the vegetation. The INSAR imagery is first adaptively filtered to reduce random measurement noise. We then combine the INSAR with polarimetric radar and laser altimetry data to account for the vegetation contribution to the topographic heights.