nils-herrmann/clean_scirepeval huggingface dataset:
Blind Source Separation with a Time-Varying Mixing Matrix

Blind source separation (BSS) is the process of using multiple sensors to separate multiple random signals from a received linear combination. In this paper, we apply blind source separation techniques to mixtures of digital communications signals. We predict the achievable symbol error rate when the signals are received on the same carrier frequency and blindly separated. In a wireless environment where the sources are mobile or the environment is changing, the mixing matrix will vary with time. Our primary contribution is that we identify the major source of error in separation. We extend an adaptive step size algorithm to the complex-valued case to mitigate the errors in a dynamic environment.
Optimization of vertical and horizontal beamforming kernels on the PowerPC G4 processor with AltiVec technology

Three-dimensional real-time digital sonar beamforming requires 4 to 12 GFLOPS, 1 to 2 GB of memory, and about 100 MB/s of I/O bandwidth. G.E. Allen and B.L. Evans have implemented a 4-GFLOP sonar beamformer in real-time on a Sun UltraSPARC II server with 16 333-MHz processors by utilizing the Visual Instruction Set (VIS) single-instruction multiple-data (SIMD) extensions. In this paper, we rewrite the horizontal and vertical beamforming kernels to use AltiVec SIMD extension for the PowerPC. AltiVec can execute up to four 32-bit floating-point multiply and accumulate (MAC) operations per instruction. In the PowerPC implementation, we prefetch and realign data for the I28-bit SIMD registers of AltiVec. We evaluate the performance of these beamforming kernels on the PowerPC and the UltraSPARC-II to evaluate the impact of the compiler, SIMD word alignment, and cache block alignment on performance.
Wideband monopulse spatial filtering for large receiver arrays for reverberant underwater communication channels

Underwater acoustic communication (ACOMMS) is critical for many applications including marine science, oceanographic exploration, offshore surveying/drilling, and military uses. ACOMMS data rates are usually limited by multiple propagation paths with different time delays and Doppler characteristics. It is often difficult to coherently recombine all paths, especially in shallow water, leaving incoherent paths that interfere with the receiver. One way to suppress unwanted paths is with a directional receiving array. Indeed, many existing large, directional acoustic arrays could be used as ACOMMS receivers. In a number of these arrays, wideband monopulse outputs could be made available. These directional beam outputs, in monopulse pairs, can selectively suppress, or even null, offending multipath when combined with a simple scalar weight. Using an experimental system, we show how a relatively short equalizer, using as inputs the wideband monopulse beam outputs of a large array, can form the backbone of an ACOMMS system that performs effectively in a multipath-limited environment. Our contributions include (i) a multipath-Doppler channel model validated by experimental results, (ii) a receiver design that utilizes monopulse processing, and (iii) an analysis of its performance using simulated and experimental data.
Advanced filter design

Classical filter design techniques return only one design from an infinite collection of alternative designs, or fail to design filters when solutions exist. These classical techniques hide a wealth of alternative filter designs that are more robust when implemented in analog circuits, digital hardware, and embedded software. We present (1) case studies of optimal analog and digital IIR filters that cannot be designed with classical techniques, and (2) the formal, mathematical framework that underlies their solutions. We have automated the advanced filter design techniques in software.
Interactive solution sets as components of fully electronic signals and systems courseware

The use of computers in teaching signals and systems has evolved. In understanding algorithms, computers were used first as simulation environments and then as real-time implementation environments. In learning the theory of signals and systems, passive tutorial systems have become interactive. Now, software is becoming more and more of an integral part of textbooks and may eventually include the textbook. The paper discusses two interdependent fundamental components of fully electronic courseware-the development of interactive problems and examples, and the supporting software to evaluate student solutions and to explain steps in a correct solution.<<ETX>>
Quality assessment of compression techniques for synthetic aperture radar images

Synthetic aperture radar (SAR) systems are mounted on airplanes and satellites, which have limited downlink and storage capacity, yet SAR image sequences may be produced at rates of several Gbps. Compression is difficult because SAR images contain significant high-frequency information, such as terrain boundaries and terrain texture. In assessing the quality of compressed images, peak signal-to-noise ratio and mean-squared error are inadequate because they assume that distortion is solely due to image-independent additive noise. In this paper, we provide objective measures to assess the visual quality of SAR images compressed by JPEG and SPIHT coders. The human visual system responds differently to linear distortion and noise injection (nonlinear distortion plus additive noise). Our key contributions are that we (1) decouple and quantify the linear distortion and noise injection in JPEG and SPIHT coders, and (2) introduce a new edge correlation quality measure which we use to quantify nonlinear distortion.
Optimal OFDMA Subcarrier, Rate, and Power Allocation for Ergodic Rates Maximization 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 OFDMA systems assuming the availability of only partial (imperfect) CSI. We consider ergodic weighted sum discrete rate maximization subject to total power constraints. We approach this problem using a dual optimization framework, allowing us to solve this problem 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 10-3 (99.999% optimal).
Deadlock detection for distributed process networks

The process network (PN) model, which consists of concurrent processes communicating over first-in first out unidirectional queues, is useful for modeling and exploiting functional parallelism in streaming data applications. The PN model maps easily onto multi-processor and/or multi-threaded targets. Since the PN model is Turing complete, memory requirements cannot be predicted statically. In general, any bounded-memory scheduling algorithm for this model requires run-time deadlock detection. The few PN implementations that perform deadlock detection detect only global deadlocks. Not all local deadlocks, however, will cause a PN system to reach global deadlock. In this paper, we present the first local deadlock detection algorithm for PN models. The proposed algorithm is based on the Mitchell and Merritt algorithm and is suitable for both parallel and distributed PN implementations.
Unification and evaluation of equalization structures and design algorithms for discrete multitone modulation systems

To ease equalization in a multicarrier system, a cyclic prefix (CP) is typically inserted between successive symbols. When the channel order exceeds the CP length, equalization can be accomplished via a time-domain equalizer (TEQ), which is a finite impulse response (FIR) filter. The TEQ is placed in cascade with the channel to produce an effective shortened impulse response. Alternatively, a bank of equalizers can remove the interference tone-by-tone. This paper presents a unified treatment of equalizer designs for multicarrier receivers, with an emphasis on discrete multitone systems. It is shown that almost all equalizer designs share a common mathematical framework based on the maximization of a product of generalized Rayleigh quotients. This framework is used to give an overview of existing designs (including an extensive literature survey), to apply a unified notation, and to present various common strategies to obtain a solution. Moreover, the unification emphasizes the differences between the methods, enabling a comparison of their advantages and disadvantages. In addition, 16 different equalizer structures and design procedures are compared in terms of computational complexity and achievable bit rate using synthetic and measured data.
Rules for multidimensional multirate structures

Identifies a comprehensive set of compact rules and efficient algorithms for simplifying and rearranging structures common in multidimensional multirate signal processing. The authors extend the 1D rules reported by Crochiere and Rabiner (1983), especially the many equivalent forms of cascades of upsamplers and downsamplers. They also include rules reported by other authors for completeness. The extension to mD is based primarily on the Smith form decomposition of resampling (nonsingular integer square) matrices. The Smith form converts non-separable multidimensional operations into separable ones by means a shuffling of input samples and a reshuffling of the separable operations. Based on the Smith form, the authors have developed algorithms for 1) computing coset vectors 2) finding greatest common sublattices 3) simplifying cascades of up/downsampling operations. The algorithms and rules are put together in a form that can be implemented efficiently in a symbolic algebra package. The authors have encoded the knowledge in the commercially available Mathematica environment.
Statistical Modeling of Co-Channel Interference

With increasing spatial reuse of the radio spectrum, co-channel interference is becoming the 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. Statistical modeling of interference is a useful tool to analyze the outage probabilities in wireless networks and to design interference-aware transceivers. Our contributions include (1) developing a unified framework to derive interference models for various wireless network environments, (2) demonstrating the applicability of the symmetric alpha stable and Middleton Class A distributions in modeling co-channel interference in ad-hoc and cellular network environments, and (3) deriving analytical conditions on the system model parameters for which these distributions accurately model the statistical properties of the interference. Simulation results allow us to compare the key properties of empirical co-channel interference and their statistical models under different wireless network environments.
A novel gradient induced main subject segmentation algorithm for digital still cameras

When taking pictures, professional photographers employ a variety of composition rules. In automating these rules, it is often first necessary to detect and segment the main subject. We propose an detection and segmentation algorithm that leverages the optics in a digital still camera. Based on where the user points the camera, an autofocus filter first puts the main subject in focus and takes a picture. Then, we open the shutter aperture to diffuse light from objects that are out-of-focus, which blurs the background, and take a second picture. Using the second picture, the resulting difference in the frequency content of the main subject and the background image is then used by the proposed algorithm to detect and segment the main subject. The algorithm does not depend on prior knowledge of the indoor/outdoor setting or scene content. Algorithm complexity is similar to that of a 5/spl times/5 filter.
ADC bit allocation under a power constraint for mmWave massive MIMO communication receivers

Millimeter wave (mmWave) systems operating over a wide bandwidth and using a large number of antennas impose a heavy burden on power consumption. In a massive multiple-input multiple-output (MIMO) uplink, analog-to-digital converters (ADCs) would be the primary consumer of power in the base station receiver. This paper proposes a bit allocation (BA) method for mmWave multi-user (MU) massive MIMO systems under a power constraint. We apply ADCs to the outputs of an analog phased array for beamspace projection to exploit mmWave channel sparsity. We relax a mean square quantization error (MSQE) minimization problem and map the closed-form solution to non-negative integer bits at each ADC. In link-level simulations, the proposed method gives better communication performance than conventional low-resolution ADCs for the same or less power. Our contribution is a near optimal low-complexity BA method that minimizes total MSQE under a power constraint.
Multiscale adaptive estimation for fusing interferometric radar and laser altimeter data

Interferometric synthetic aperture radar (INSAR) data are fused with laser altimeter (LIDAR) data to produce improved estimates of bare-surface topography and vegetation heights. The data from both sensors are first transformed into estimates of surface elevations and vegetation heights to obtain linear measurement-state relations. A spatially-adaptive multiscale estimation framework is then used to combine the data, which were acquired at different resolutions. The estimation is performed in scale and space via a set of Kalman filters. It yields better error characteristics than the nonadaptive multiscale filter and accommodates non-stationarity in the image data.
Sinusoidal Modeling and Adaptive Channel Prediction in Mobile OFDM Systems

We propose a wireless fading channel prediction algorithm for a pilot-symbol aided orthogonal frequency division multiplexing (OFDM) system. Assuming a doubly selective (time and frequency varying) ray-based physical channel model and equispaced pilot subcarriers in time and frequency, this algorithm performs channel model parameter acquisition using a 2-Step 1-D ESPRIT (estimation of signal parameters via rotational invariance techniques) as a first stage, and channel prediction via model extrapolation as a second stage. Since the channel model parameter acquisition has cubic complexity, we also propose a linear complexity channel parameter tracking algorithm based on an improved adaptive ESPRIT algorithm to continuously adapt to the time-varying channel model parameters. We derive the Cramer-Rao lower bound (CRLB) and asymptotic CRLB (ACRLB) for the mean squared error (MSE) in OFDM channel prediction. We show that our proposed OFDM channel prediction algorithm has better MSE performance while maintaining similar computational complexity than previous methods in sparse multipath fading channels characterized by specular scattering, which is most suitable for outdoor mobile macrocell scenarios. Thus, our method can be seen as a complement to the existing schemes that are more suitable for dense multipath channels with diffuse scattering, which is typical of urban pico-cell and indoor wireless scenarios. We provide simulation results based on the IEEE 802.16e mobile broadband wireless access standard to corroborate our claims.
No-reference image quality assessment for high dynamic range images

Being able to automatically predict digital picture quality, as perceived by human observers, has become important in many applications where humans are the ultimate consumers of displayed visual information. Standard dynamic range (SDR) images provide 8 bits/color/pixel. High dynamic range (HDR) images which are usually created from multiple exposures of the same scene, can provide 16 or 32 bits/color/pixel, but must be tonemapped to SDR for display on standard monitors. Multi-exposure fusion (MEF) techniques bypass HDR creation, by fusing the exposure stack directly to SDR format while aiming for aesthetically pleasing luminance and color distributions. Here we describe a new no-reference image quality assessment (NR IQA) model for HDR pictures that is based on standard measurements of the bandpass and on newly-conceived differential natural scene statistics (NSS) of HDR pictures. We derive an algorithm from the model which we call the Gradient Image Quality Assessment algorithm (G-IQA). NSS models have previously been used to devise NR IQA models that effectively predict the subjective quality of SDR images, but they perform significantly worse on tonemapped HDR content. Towards ameliorating this we make here the following contributions: (1) We design a HDR picture NR IQA model and algorithm using both standard space-domain NSS features as well as novel HDR-specific gradient based features that significantly elevate prediction performance, (2) We validate the proposed models on a large-scale crowdsourced HDR image database, and (3) We demonstrate that the proposed model also perform well on legacy natural SDR images. The software is available at: http://signal.ece.utexas.edu/%7Edebarati/higradeRelease.zip.
No-Reference Quality Assessment of Tone-Mapped HDR Pictures

Being able to automatically predict digital picture quality, as perceived by human observers, has become important in many applications where humans are the ultimate consumers of displayed visual information. Standard dynamic range (SDR) images provide 8 b/color/pixel. High dynamic range (HDR) images, which are usually created from multiple exposures of the same scene, can provide 16 or 32 b/color/pixel, but must be tonemapped to SDR for display on standard monitors. Multi-exposure fusion techniques bypass HDR creation, by fusing the exposure stack directly to SDR format while aiming for aesthetically pleasing luminance and color distributions. Here, we describe a new no-reference image quality assessment (NR IQA) model for HDR pictures that is based on standard measurements of the bandpass and on newly conceived differential natural scene statistics (NSS) of HDR pictures. We derive an algorithm from the model which we call the HDR IMAGE GRADient-based Evaluator. NSS models have previously been used to devise NR IQA models that effectively predict the subjective quality of SDR images, but they perform significantly worse on tonemapped HDR content. Toward ameliorating this we make here the following contributions: 1) we design HDR picture NR IQA models and algorithms using both standard space-domain NSS features as well as novel HDR-specific gradient-based features that significantly elevate prediction performance; 2) we validate the proposed models on a large-scale crowdsourced HDR image database; and 3) we demonstrate that the proposed models also perform well on legacy natural SDR images. The software is available at: http://live.ece.utexas.edu/research/Quality/higradeRelease.zip.
Optimal weight vectors for broadcast channels

In a multi-transmitter broadcast system, the weight vector for each message signal can provide an additional degree-of-freedom for signal enhancement and interference suppression by taking advantage of the spatial diversity among the users. To date, the design of the optimal weight vectors which maximize the overall channel capacity is still an open problem. The main reason is attributed to the fact that under certain power constraints, the channel capacity R is a highly nonlinear function of the M-dimensional weight vectors {w/sub i/}, where M is the number of transmitters. Therefore, the maximization of R over {w/sub i/} does not seem to be tractable to simple mathematic treatment. In this paper, we present our studies on designing optimal weight vectors for a two-user, multi-transmitter broadcast channel. Our primary contribution is to decouple the weight vectors in R which simplifies the optimization problem to a search for the maxima of a smooth two-dimensional function.
GNSS Signal Authentication Via Power and Distortion Monitoring

We propose a simple low-cost technique that enables civil global positioning system receivers and other civil global navigation satellite system (GNSS) receivers to reliably detect carry-off spoofing and jamming. The technique, which we call the power-distortion detector, classifies received signals as interference-free, multipath-afflicted, spoofed, or jammed according to observations of received power and correlation function distortion. It does not depend on external hardware or a network connection and can be readily implemented on many receivers via a firmware update. Crucially, the detector can with high probability distinguish low-power spoofing from ordinary multipath. In testing against more than 25 high-quality empirical datasets yielding more than 900,000 separate detection tests, the detector correctly alarms on all malicious spoofing or jamming attacks while maintaining a  $<$0.6% single-channel false alarm rate.
Joint optimization of multiple behavioral and implementation properties of digital IIR filter designs

This paper presents an extensible framework for the simultaneous constrained optimization of multiple properties of digital IIR filters. The framework optimizes the pole-zero locations for behavioral properties of magnitude and phase response, and the implementation property of quality factors, subject to constraints on the same properties. We formulate the constrained nonlinear optimization problem as a sequential quadratic programming (SQP) problem. SQP solvers are robust when providing formulas for the gradients of the objective function and constraints. We program Mathematica to compute the gradient formulas and convert the formulas into Matlab programs to perform the optimization, The automated approach eliminates errors in manipulating the algebraic equations and transcribing equations into software. The key contributions are (1) an automated, extensible, multicriteria digital IIR filter optimization framework, and (2) a novel filter design. We have released the source code on the Internet.
Stochastic Modeling of Microwave Oven Interference in WLANs

An IEEE 802.11b/g/n wireless local area network (WLAN) experiences significant radio frequency interference (RFI) from microwave ovens, cordless phones, Bluetooth, and other WLANs operating in the 2.4 GHz band. In the 2.4 GHz band, operating microwave ovens emit interference that can either prevent an access point from transmitting or cause dramatic increase in bit errors in the receiver. This paper reduces disruption in real-time transmission and reception of high-definition streaming video by mitigating oven-generated RFI. The proposed contributions of this paper include (1) a statistical-physical model of oven-generated RFI in WLAN channels that is dependent on distance and frequency, (2) an increase in the information rate bound of up to 5 bits/s/Hz due to the more realistic statistical-physical model of oven-generated RFI, and (3) a proposed transmission strategy to achieve the higher information rate bound.
A Distributed Deadlock Detection and Resolution Algorithm for Process Networks

In the process network (PN) model, multiple concurrent processes communicate over unidirectional FIFO queues. PN is useful for modeling signal processing systems of streaming data, and naturally captures parallelism in these systems. PN provides formal execution properties to alleviate the difficulties of threaded and distributed programming, and naturally maps onto parallel and distributed targets. For a large class of PN, clever run-time scheduling can permit execution in bounded memory. In general, PN termination and boundedness cannot be statically determined, so correct bounded scheduling of PN requires run-time deadlock detection. We present the first algorithm that correctly performs dynamic deadlock detection and resolution for bounded scheduling of PN. The proposed algorithm is a modification of a distributed deadlock detection algorithm by Mitchell and Merritt.