nils-herrmann/clean_scirepeval huggingface dataset:
Constrained 3D Rotation Smoothing via Global Manifold Regression for Video Stabilization

We present a novel motion smoothing algorithm for hand-held cameras with application to video stabilization. Video stabilization seeks to remove unwanted frame-to-frame jitter due to camera shake. For video stabilization, we use a pure 3D rotation motion model with known camera projection parameters. The 3D camera rotation can be reliably tracked by a gyroscope as commonly found on a smart phone or tablet. In this paper, we directly smooth the sequence of camera rotation matrices for the video frames by exploiting the Riemannian geometry on a manifold. Our contributions are 1) formulation of motion smoothing as a geodesic-convex constrained regression problem on a nonlinear manifold based on geodesic distance, 2) computation of gradient and Hessian of the objective function using Riemannian geometry for gradient-related manifold optimization, and 3) generalization of the two-metric projection algorithm in Euclidean space to manifolds to solve the proposed manifold optimization problem efficiently. The geodesic-distance-based smoothness metric better exploits the manifold structure of sequences of rotation matrices. The geodesic-convex constraints effectively guarantee that no black borders intrude into the stabilized frames. The proposed manifold optimization algorithm can find the global optimal solution in only a few iterations. Experimental results show that video stabilization based on our motion smoothing algorithm outperforms state-of-the-art methods by generating videos with less jitter and without black borders.
A self-recovering RAKE receiver for asynchronous CDMA systems

In asynchronous CDMA systems, transmission over frequency-selective channels subjects the signal to interchip interference and intersymbol interference, both of which cause multiple access interference (MAI). MAI cannot be easily eliminated without the knowledge of the channel parameters. Both blind adaptive and subspace based multiuser receivers have been proposed to eliminate the interference. We propose a self-recovering RAKE receiver that does not require training sequences or knowledge of interfering user signature waveforms. The contribution of this paper is the combination of constant modulus algorithm and previously derived constrains. The constrains depend on the multipath structure of the desired signal. The proposed receiver converges faster than the blind adaptive multiuser receivers. Its fast convergence and low complexity make it well suited for rapidly time-varying mobile radio frequency selective fading channels. By simulation, we compare the proposed method with blind adaptive constrained LMS RAKE receiver and minimum output energy receiver.
Loss of perfect reconstruction in multidimensional filterbanks and wavelets designed via extended McClellan transformations

Two extensions of the McClellan transformation have been proposed to transform even-length symmetric one-dimensional (1-D) prototype filters into even/odd-length multidimensional filters. We show that, unlike the original McClellan transformation, the two extended McClellan transformations suffer from a limitation in the design of multidimensional two-channel finite impulse response (FIR) perfect reconstruction (PR) filterbanks and wavelets in the sense that there does not exist any transformation function that can preserve PR after transformation, However, for one of the transformations, we point out that near-PR filterbanks are possible.
Outage Probability for Diversity Combining in Interference-Limited Channels

Many wireless data communication systems such as LTE, and Wi-Fi, are increasingly facing interference that is much stronger than thermal noise. Interference may arise due to dense spatial reuse of spectrum intended to increase user data rates, or from other devices emitting radiation in the same spectrum, or from electronic circuitry within the communication platform. For a multi-antenna receiver operating in an interference-limited channel, we evaluate four diversity combining algorithms in terms of outage probability in the low-outage regime. The contributions of this paper are (1) derivation of closed-form expressions for the output signal-to-interference ratio (SIR) statistics of fixed weight, maximal ratio, selection and post-detection combining; (2) comparison of the relative outage performance of these algorithms; and (3) proposed diversity combining algorithms to reduce outage probability. Our results can be applied in analyzing the outage performance and throughput capacity of both centralized and decentralized interference-limited wireless networks.
Robust perceptual image hashing using feature points

Perceptual image hashing maps an image to a fixed length binary string based on the image's appearance to the human eye, and has applications in image indexing, authentication, and watermarking. We present a general framework for perceptual image hashing using 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 enhance perceptual robustness further. 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.
Online Camera-Gyroscope Autocalibration for Cell Phones

The gyroscope is playing a key role in helping estimate 3D camera rotation for various vision applications on cell phones, including video stabilization and feature tracking. Successful fusion of gyroscope and camera data requires that the camera, gyroscope, and their relative pose to be calibrated. In addition, the timestamps of gyroscope readings and video frames are usually not well synchronized. Previous paper performed camera-gyroscope calibration and synchronization offline after the entire video sequence has been captured with restrictions on the camera motion, which is unnecessarily restrictive for everyday users to run apps that directly use the gyroscope. In this paper, we propose an online method that estimates all the necessary parameters, whereas a user is capturing video. Our contributions are: simultaneous online camera self-calibration and camera-gyroscope calibration based on an implicit extended Kalman filter and generalization of the multiple-view coplanarity constraint on camera rotation in a rolling shutter camera model for cell phones. The proposed method is able to estimate the needed calibration and synchronization parameters online with all kinds of camera motion and can be embedded in gyro-aided applications, such as video stabilization and feature tracking. Both Monte Carlo simulation and cell phone experiments show that the proposed online calibration and synchronization method converge fast to the ground truth values.
Combining interferometric radar and laser altimeter data to improve estimates of topography

Interferometric synthetic aperture radar (INSAR) has been used to map terrain topography; however, accuracy is 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. The authors solve an inverse problem for INSAR scattering to determine surface and vegetation elevations, and then incorporate sparse laser altimeter observations to improve the estimates of surface and vegetation elevations.
A low complexity algorithm for proportional resource allocation in OFDMA systems

Orthogonal frequency division multiple access (OFDMA) basestations allow multiple users to transmit simultaneously on different subcarriers during the same symbol period. This paper considers basestation allocation of subcarriers and power to each user to maximize the sum of user data rates, subject to constraints on total power, bit error rate, and proportionality among user data rates. Previous allocation methods have been iterative nonlinear methods suitable for offline optimization. In the special high subchannel SNR case, an iterative root-finding method has linear-time complexity in the number of users and N log N complexity in the number of subchannels. We propose a non-iterative method that is made possible by our relaxation of strict user rate proportionality constraints. Compared to the root-finding method, the proposed method waives the restriction of high subchannel SNR, has significantly lower complexity, and in simulation, yields higher user data rates.
Full-reference visual quality assessment for synthetic images: A subjective study

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. For assessing subjective quality of natural images, such as those taken by optical cameras, significant progress has been made for several decades. To aid in the benchmarking of objective image quality assessment (IQA) algorithms, many natural image databases have been annotated with subjective ratings of the images by human observers. Similar information, however, is not as readily available for synthetic images commonly found in video games and animated movies. In this paper, our primary contributions are (1) conducting subjective tests on our publicly available ESPL Synthetic Image Database, and (2) evaluating the performance of more than 20 full reference IQA algorithms for natural images on the synthetic image database. The ESPL Synthetic Image Database contains 500 distorted images (20 distorted images for each of the 25 original images) in 1920 × 1080 format. After collecting 26000 individual human ratings, we compute the differential mean opinion score (DMOS) for each image to evaluate IQA algorithm performance.
Non-parametric mitigation of periodic impulsive noise in narrowband powerline communications

Periodic impulsive noise synchronous to the main powerline frequency is the dominant noise component in OFDM-based narrowband (NB) powerline communications (PLC). Such noise occurs in periodic bursts, where a single burst could corrupt multiple OFDM symbols. Standardized NB PLC systems use frequency-domain interleaving (FDI) in combination with forward error correction to combat impulsive noise. Alternate designs adopt time-domain block interleaving (TDI) in which the receiver deinterleaver scatters an impulsive noise burst into short impulses over a large number OFDM symbols. In bursty impulsive noise, TDI-OFDM (FDI-OFDM) works better at high (low) SNR. In this paper, we develop non-parametric methods for periodic impulsive noise mitigation in coded TDI-OFDM systems. We exploit the sparse structure of the time-domain noise after the deinterleaver, and propose sparse Bayesian learning based algorithms that estimate and remove the noise impulses by observing the null and pilot tones of received signal and using decision feedback from the decoder. The proposed methods do not assume any statistical noise model and hence do not require any training. In simulations, the proposed methods in TDI-OFDM systems achieve up to 6 dB SNR gain over FDI-OFDM systems at typical NB PLC SNR values.
Training neural hardware with noisy components

Some next generation computing devices may consist of resistive memory arranged as a crossbar. Currently, the dominant approach is to use crossbars as the weight matrix of a neural network, and to use learning algorithms that require small incremental weight updates, such as gradient descent (for example Backpropagation). Using real-world measurements, we demonstrate that resistive memory devices are unlikely to support such learning methods. As an alternative, we offer a random search algorithm tailored to the measured characteristics of our devices.
Clustering algorithms for perceptual image hashing

A perceptual image hash function maps an image to a short binary string, based on an image's appearance to the human eye. Perceptual image hashing is useful in image databases, watermarking, and authentication. In this paper, we decouple image hashing into feature extraction (intermediate hash) followed by data clustering (final hash). For any perceptually significant feature extractor, we propose a polynomial-time heuristic clustering algorithm that automatically determines the final hash length needed to satisfy a specified distortion. We prove that the decision version of our clustering problem is NP complete, Based on the proposed algorithm, we develop two variations to facilitate perceptual robustness vs. fragility trade-offs. We test the proposed algorithms against Stirmark attacks.
Comparison of space-time water-filling and spatial water-filling for MIMO fading channels

We compare the capacities achieved by space-time water-filling and spatial water-filling for MIMO fading channels. Both the effects of fast fading and shadowing are considered. It is found that for Rayleigh fast fading MIMO channels, the spectral efficiency per antenna achieved by one-dimensional spatial water-filling is close to two-dimensional space-time water-filling. However, with log-normal shadowing, space-time water-filling achieves significantly higher capacity per antenna than spatial water-filling at low to moderate SNR regimes. Furthermore, space-time water-filling has lower computational complexity than spatial water-filling. It is also shown that space-time water-filling requires a priori knowledge of the channel gain distribution, and for Rayleigh channels with log-normal shadowing, the spectral efficiency advantage over spatial water-filling comes with an increased channel outage probability.
Equalization for discrete multitone transceivers to maximize bit rate

In a discrete multitone receiver, a time-domain equalizer (TEQ) reduces the intersymbol interference (ISI) by shortening the effective duration of the channel impulse response. Current TEQ design methods such as the minimum mean-squared error (MMSE), maximum shortening SNR (MSSNR), and maximum geometric SNR (MGSNR) do not directly maximize bit rate. We develop two TEQ design methods to maximize the bit rate. First, we partition an equalized multicarrier channel into its equivalent signal, noise, and ISI paths to develop a new subchannel SNR definition. Then, we derive a nonlinear function of TEQ taps that measures the bit rate, which the proposed maximum bit rate (MBR) method optimizes. We also propose a minimum-ISI method that generalizes the MSSNR method by weighting the ISI in the frequency domain to obtain higher performance. The minimum-ISI method is amenable to real-time implementation on a fixed-point digital signal processor. Based on simulations using eight different carrier-serving-area loop channels, (1) the proposed methods yield higher bit rates than MMSE, MGSNR, and MSSNR methods; (2) the proposed methods give three-tap TEQs with higher bit rates than 17-tap MMSE, MGSNR, and MSSNR TEQs; (3) the proposed MBR method achieves the channel capacity (as computed by the matched filter bound using the proposed subchannel SNR model) with a five-tap TEQ; and (4) the proposed minimum-ISI method achieves the bit rate of the optimal MBR method.
Sum Capacity of Multiuser MIMO Broadcast Channels with Block Diagonalization

The sum capacity of a Gaussian broadcast MIMO channel can be achieved with dirty paper coding (DPC). Deploying DPC in real-time systems is, however, impractical. Block diagonalization (BD) is an alternative precoding technique for downlink multiuser MIMO systems, which can eliminate interuser interference at each receiver, at the expense of suboptimal sum capacity vs. DPC. In this paper, we study the sum capacity loss of BD for a fixed channel. We show that 1) if the user channels are orthogonal to each other, then BD achieves the complete sum capacity; and 2) if the user channels lie in a common row vector space, then the gain of DPC over BD can be bounded by the minimum of the number of transmit and receive antennas and the number of users. We also compare the ergodic sum capacity of DPC with that of BD in a Rayleigh fading channel. Simulations show that BD can achieve a significant part of the total throughput of DPC. An upper bound on the ergodic sum capacity gain of DPC over BD is derived, which can be evaluated with a few numerical integrations. With this bound, we can easily estimate how far away BD is from being optimal in terms of ergodic sum capacity, which is useful in directing practical system designs
A dual-path TEQ structure for DMT-ADSL systems

Discrete Multitone (DMT) modulation is the industry standard for Asymmetrical Digital Subscriber Loop (ADSL) modems. DMT modulation allows for simplified equalization when the effective duration of the channel memory is shorter than the predefined length of the cyclic prefix (CP). A single time-domain equalizer (TEQ) is typically used to truncate the channel memory such that the data rate loss due to the use of the CP is reduced. Because the single TEQ is optimized over all sub channels, it is difficult to find an ideal solution in which the number of bits supported on each sub channel is optimized. This paper proposes a dual-path TEQ, which adds a second TEQ that is biased to selected sub channels to increase transmission throughput. The implementation complexity is lower than other band-partition equalization methods such as per tone equalization.
Statistical modeling of co-channel interference in a field of Poisson distributed interferers

With increasing spatial reuse of the 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 annulus field of Poisson interferers. Our contributions include (1) demonstrating the applicability of the symmetric alpha stable and Middleton Class A distributions in modeling co-channel interference in various topologies of interferers, and (2) deriving analytical conditions on the system parameters for which these distributions accurately model the interference statistics. Through simulation, we compare the decay rate of tail probabilities of the empirical co-channel interference and the symmetric alpha stable, Middleton Class A, and Gaussian models for different topologies of interferers. Practical applications include co-channel interference modeling for various wireless network environments, including ad hoc and cellular networks.
A combined symmetric difference and power monitoring GNSS anti-spoofing technique

Civil Global Navigation Satellite System (GNSS) signals are vulnerable to spoofing attacks that deceive a victim receiver into reporting counterfeit position or time information. The primary contribution of this paper is a non-cryptographic GNSS anti-spoofing technique that “sandwiches” a spoofer between a correlation function distortion monitor and a total in-band power monitor. The defense exploits the difficulty of mounting an effective spoofing attack that simultaneously maintains a low-enough counterfeit signal power to avoid power monitoring alarms while minimizing distortions of the received cross-correlation profile that are indicative of a spoofing attack. Results presented in this paper demonstrate the defense's effectiveness against a sophisticated spoofing attack.
Large-Scale Crowdsourced Study for Tone-Mapped HDR Pictures

Measuring digital picture quality, as perceived by human observers, is increasingly important in many applications in which humans are the ultimate consumers of visual information. Standard dynamic range (SDR) images provide 8 b/color/pixel. High dynamic range (HDR) images, usually created from multiple exposures of the same scene, can provide 16 or 32 b/color/pixel, but need to be tonemapped to SDR for display on standard monitors. Multiexposure fusion (MEF) techniques bypass HDR creation by fusing an exposure stack directly to SDR images to achieve aesthetically pleasing luminance and color distributions. Many HDR and MEF databases have a relatively small number of images and human opinion scores, obtained under stringently controlled conditions, thereby limiting realistic viewing. Moreover, many of these databases are intended to compare tone-mapping algorithms, rather than being specialized for developing and comparing image quality assessment models. To overcome these challenges, we conducted a massively crowdsourced online subjective study. The primary contributions described in this paper are: 1) the new ESPL-LIVE HDR Image Database that we created containing diverse images obtained by tone-mapping operators and MEF algorithms, with and without post-processing; 2) a large-scale subjective study that we conducted using a crowdsourced platform to gather more than 300 000 opinion scores on 1811 images from over 5000 unique observers; and 3) a detailed study of the correlation performance of the state-of-the-art no-reference image quality assessment algorithms against human opinion scores of these images. The database is available at http://signal.ece.utexas.edu/%7Edebarati/HDRDatabase.zip.
Doppler estimation and correction for shallow underwater acoustic communications

Reliable mobile underwater acoustic communication systems must compensate for strong, time-varying Doppler effects. Many Doppler correction techniques rely on a single bulk correction to compensate first-order effects. In many cases, residual higher-order effects must be tracked and corrected using other methods. The contributions of this paper are evaluations of (1) signal-to-noise ratio (SNR) performance from three Doppler estimation and correction methods and (2) communication performance of Doppler correction with static vs. adaptive equalizers. The evaluations use our publicly available shallow water experimental dataset, which consists of 360 packet transmission samples (each 0.5s long) from a five-channel receiver array.
DMT bit rate maximization with optimal time domain equalizer filter bank architecture

In a multicarrier modulation system, a time domain equalizer (TEQ) traditionally shortens the transmission channel impulse response (CIR) to mitigate intersymbol interference (ISI). In this paper, we propose a data-rate optimal TEQ filter bank whose data rates at the equalizer output of this filter bank are significantly better than those of the Maximum Bit Rate and Minimum ISI methods and similar to those of the least-squares per-tone equalizer method for ADSL CIRs, transmit filters, and receive filters. The contributions of this study are: (1) a new model for multicarrier demodulator output SNR that includes ISI, near-end crosstalk, white Gaussian noise, and the digital noise floor, (2) data rate optimal time domain per-tone TEQ filter bank, and (3) a new achievable upper bound on achievable bit rate.