nils-herrmann/clean_scirepeval huggingface dataset:
Mitigating near-field interference in laptop embedded wireless transceivers

In laptop and desktop computers, clocks and busses generate significant radio frequency interference (RFI) for the embedded wireless data transceivers. RFI is impulsive in nature. When detecting a signal in additive impulsive noise, Spaulding and Middleton showed a potential improvement in detection of 25 dB at a bit error rate of 10-5 when using a Bayesian detector instead of a standard correlation receiver. In this paper, we model impulsive noise using Middleton class A and symmetric alpha stable (SaS) models. The contributions of this paper are to evaluate (1) the performance vs. complexity of parameter estimation algorithms, (2) the closeness of fit of parameter estimation algorithms to measured RFI data from the computer platform, (3) the communication performance vs. computational complexity tradeoffs for the correlation receiver, Wiener filter, and Bayesian detector, and (4) the performance of myriad filtering in combating RFI interference modeled as SaS interference.
A fingerprint classification technique using directional images

We present a fast, automated, feature-based technique for classifying fingerprints. The technique extracts the singular points (delta and core points) in fingerprints obtained from directional histograms. The technique enhances the digitized image using adaptive clipping and image matching, finds the directional image by checking the orientations of individual pixels, computes directional histograms using overlapping blocks in the directional image, and classifies the fingerprint into the Wirbel class (whorl and twin loop) or the Lasso class (arch, tented arch, right loop, or left loop). The complexity of the technique is on the order of the number of pixels in the fingerprint image. The technique does not require iterations or feedback, and is highly parallel.
Image Authentication Under Geometric Attacks Via Structure Matching

Surviving geometric attacks in image authentication is considered to be of great importance. This is because of the vulnerability of classical watermarking and digital signature based schemes to geometric image manipulations, particularly local geometric attacks. In this paper, we present a general framework for image content authentication using salient feature points. We first develop an iterative feature detector based on an explicit modeling of the human visual system. Then, we compare features from two images by developing a generalized Hausdorff distance measure. The use of such a distance measure is crucial to the robustness of the scheme, and accounts for feature detector failure or occlusion, which previously proposed methods do not address. The proposed algorithm withstands standard benchmark (e.g. Stirmark) attacks including compression, common signal processing operations, global as well as local geometric transformations, and even hard to model distortions such as print and scan. Content changing (malicious) manipulations of image data are also accurately detected
Implementation Complexity and Communication Performance Tradeoffs in Discrete Multitone Modulation Equalizers

Several high-speed communication standards modulate encoded data on multiple-carrier frequencies using the fast Fourier transform (FFT). The real part of the quantized inverse FFT samples form a symbol. The symbol is periodically extended by prepending a copy of its last few samples, also known as a cyclic prefix. When the cyclic prefix is longer than the channel order, amplitude and phase distortion can be equalized entirely in the frequency domain. In the receiver, prior to the FFT, a time-domain equalizer, in the form of a finite-impulse response filter, shortens the effective channel impulse response. Alternately, a bank of equalizers tuned to each carrier frequency can be used. In earlier literature, we unified optimal multicarrier equalizer design algorithms as a product of generalized Rayleigh quotients. In this paper, we convert the unified theoretical framework into a framework for fast design algorithms. The relevant literature is reviewed and classified according to this framework. We analyze the achieved bit rate versus implementation complexity (in terms of multiply-and-accumulate operations) tradeoffs in the original and fast design algorithms. The comparison includes multiple implementations of each of 16 different equalizer structures and design algorithms using synthetic and measured discrete multitone modulated data
MIMO Receiver Design in the Presence of Radio Frequency Interference

Multi-input multi-output (MIMO) receivers have been designed and their communication performance analyzed under the assumption of additive Gaussian noise. Wireless transceivers, however, may be affected by radio frequency interference (RFI) that is well modeled using non-Gaussian impulsive statistics. In this paper, we consider the problem of receiver design for a two transmit, two receive antenna MIMO system in the presence of RFI. First, we show that RFI is well modeled using a bivariate Middleton Class A model and validate the model with measured data. Using this RFI model, we demonstrate that conventional MIMO receivers experience significant degradation in communication performance. Then we derive the maximum likelihood (ML) receiver assuming bivariate Middleton Class A noise. Furthermore, we develop a parameter estimation method for this noise model and propose two sub-optimal ML receivers with reduced computational complexity. Simulations show significant improvement in symbol error rate performance of the proposed techniques over receivers designed assuming additive Gaussian noise.
SPC03-4: Exploiting Spatio-Temporal Correlations in MIMO Wireless Channel Prediction

We investigate prediction algorithms that exploit both temporal and spatial correlations in MIMO correlated narrowband fading wireless channels. We first derive the optimal two dimensional minimum mean square error (2D-MMSE) prediction filter that maximally exploits the available temporal and spatial correlations. We then propose a lower complexity 2-step prediction algorithm, which first exploits temporal correlations using a classical single-input single-output (SISO) MMSE time- domain prediction filter for each entry in the MIMO channel, followed by an MMSE spatial smoothing step to exploit the spatial correlations. Compared to the SISO and specular prediction approaches, this approach either achieves lower MSE with a slight increase in complexity, or comparable MSE with lower complexity, in a wide range of wireless channel conditions. The same advantage holds for the average mutual information.
Real-time DSP for sophomores

We are developing a sophomore course to serve as a first course in electrical engineering. The course focuses on discrete-time systems. Its goal is to give students an intuitive understanding of concepts such as sinusoids, frequency domain, sampling, aliasing, and quantization. In the laboratory, students build simulations and real-time systems to test these ideas. By using a combination of high-level and DSP assembly languages, the students experiment with a variety of views into the representation, design, and implementation of systems. The students are exposed to a digital style of implementation based on programming both desktop and embedded processors.
Biorthogonal quincunx Coifman wavelets

We define and construct a new family of compactly supported, nonseparable two-dimensional wavelets, "biorthogonal quincunx Coifman wavelets" (BQCWs), from their one-dimensional counterparts using the McClellan transformation. The resulting filter banks possess many interesting properties such as perfect reconstruction, vanishing moments, symmetry, diamond-shaped passbands, and dyadic fractional filter coefficients. We derive explicit formulas for the frequency responses of these filter banks. Both the analysis and synthesis lowpass filters converge to an ideal diamond-shaped halfband lowpass filter as the order of the corresponding BQCW system tends to infinity. Hence, they are promising in image and multidimensional signal processing applications. In addition, the synthesis scaling function in a BQCW system of any order is interpolating (or cardinal), which has been known as a desired merit in numerical analysis.
Channel equalization by feedforward neural networks

A signal suffers from nonlinear, linear, and additive distortion when transmitted through a channel. Linear equalizers are commonly used in receivers to compensate for linear channel distortion. As an alternative, nonlinear equalizers have the potential to compensate for all three sources of channel distortion. Previous authors have shown that nonlinear feedforward equalizers based on either multilayer perceptron (MLP) or radial basis function (RBF) neural networks can outperform linear equalizers. In this paper, we compare the performance of MLP vs. RBF equalizers in terms of symbol error rate vs. SNR. We design a reduced complexity neural network equalizer by cascading an MLP and a RBF network. In simulation, the new MLP-RBF equalizer outperforms MLP equalizers and RBF equalizers.
Generalized coiflets: a new family of orthonormal wavelets

We generalize an existing family of wavelets, coiflets, by replacing the zero-centered vanishing moment condition on scaling functions by a nonzero-centered one in order to obtain a novel class of compactly supported orthonormal wavelets (we call them generalized coiflets). This generalization offers an additional free parameter i.e., the center of mass of the scaling function, which can be tuned to obtain improved characteristics of the resulting wavelet system such as near-symmetry of the scaling functions and wavelets, near-linear phase of the filter banks, and sampling approximation properties. Therefore, these new wavelets are promising in a broad range of applications in signal processing and numerical analysis.
Statistical Modeling of Asynchronous Impulsive Noise in Powerline Communication Networks

Powerline distribution networks are increasingly being employed to support smart grid communication infrastructure and in-home LAN connectivity. However, their primary function of power distribution results in a hostile environment for communication systems. In particular, asynchronous impulsive noise, with levels as high as 50dB above thermal noise, causes significant degradation in communication performance. Much of the prior work uses limited empirical measurements to propose a statistical model for instantaneous statistics of asynchronous noise. In this paper, we (i) derive a canonical statistical-physical model of the instantaneous statistics of asynchronous noise based on the physical properties of the PLC network, and (ii) validate the distribution using simulated and measured PLC noise data. The results of this paper can be used to analyze, simulate, and mitigate the effect of the asynchronous noise on PLC systems.
Simultaneous multichannel time domain equalizer design based on the maximum composite shortening SNR

A time domain equalizer (TEQ) is a finite impulse response filter that shortens the channel impulse response (CIR) to mitigate intersymbol interference (ISI). P. Melsa et al. , p.1662-72, 1996) minimized ISI in the time domain by maximizing the shortening signal-to-noise ratio (SSNR) of the energy in a target window to the energy outside the target window in the shortened CIR. Infinite SSNR means zero ISI. Melsa et al. also developed a joint channel shortening method to design a single TEQ to shorten a channel and a near-end echo impulse response. We extend the joint SSNR method to design a single TEQ to shorten multiple channels by maximizing the composite SSNR. The composite SSNR is a weighted sum of normalized channel SSNRs. The normalized SSNR is the ratio of the energy in the target window samples to the energy of all samples in the shortened CIR and has a range of [0, 1] so that it is better suited for numerical stability and fixed-point implementation. Our proposed method outperforms the joint channel shortening method. because it achieves higher weighted sum of SSNRs of the used channels.
Real-Time MIMO Discrete Multitone Transceiver Testbed

Given a wireline transmitter/receiver pair with fixed parameters (modulation and coding methods, transmission, bandwidth, receiver noise floor, etc.) that achieves a certain bit rate on a single pair of wires, one might expect that using two pairs of wires (and two sets of transceivers) would double the bit rate. However, the presence of crosstalk induced by the coupling of the energy across the wire pairs may cause significant reduction in the expected bit rate. In this paper, we present a multiple-input multiple-output (MIMO) discrete multitone (DMT) system that achieves double the bit rate of a traditional single transmitter- receiver system when using 2 transmitters and 2 receivers operating over two wires. The improvement has been achieved both in desktop simulation and in a real-time testbed. The testbed also allows rapid exploration of many design tradeoffs in a DMT system.
Mathematica as an educational tool for signal processing

The authors describe extensions to a symbolic mathematics program that make it an effective educational tool for courses in linear systems theory. These extensions are in the form of packages and notebooks for the Mathematica environment. The new signal processing packages contain rule bases that symbolically compute convolutions and all the transforms common to signal processing: Fourier, Laplace, z, etc. In order to introduce a student to these concepts and to how they are implemented in Mathematica, four tutorials have been written in Mathematica's notebook format. Notebooks, a form of hypertext, allow students to access material randomly, to discover insights at their own pace, and to evaluate code provided in the examples. Students can also use notebooks to solve homework problems in a self-documenting fashion.<<ETX>>
Demonstration of single-sided Compton scatter tomography in fan beams with an HPGe array

An instrument is demonstrated whereby radiographic images of a sample's electron density are compiled from the information encoded in the energy spectra of gamma rays backscattered from one side of the sample. Nondestructive inspection of aluminum airframes is the application of interest, so it is assumed that access is restricted to only one surface of the object under inspection. Use of energy coding allows imaging in a fan beam rather than independent interrogation of individual volume elements. The Multiplexed Compton Scatter Tomograph (MCST) instrument consists of an array of planar high-purity germanium detectors, a set of fan beam collimators, and a Cd/sup 109/ radionuclide source. Instrument signals are converted to electron density images using a penalized weighted least squares image reconstruction algorithm coupled with a deterministic system model that includes effects of finite source and detector size, detector resolution and efficiency, attenuation, and Doppler broadening. The Doppler broadening, resulting from the momentum distribution of bound electrons, is significant at the energies used here. The proof-of-principle instrument is demonstrated on some aluminum samples. In an 8-mm thick sample with a 4 mm void in its center, contrast recovery of 90% is achieved. In a 10-mm thick sample with a 3 rum void at the back, about 85% of the contrast is recovered.
Time-domain compression of complex-baseband LTE signals for cloud radio access networks

Modern cellular networks such as Long-Term Evolution (LTE) transport complex-baseband samples between remote radio hardware and processing equipment. Common Public Radio Interface (CPRI) links are widely used in practice and enable flexible radio head deployments, distributed antenna systems, and advanced spatial processing such as coordinated multi-point (CoMP) transmission and reception. Current CPRI links already have insufficient capacity to support 20 MHz bandwidth LTE for a basestation with three sectors and four antennas per sector. By supporting eight antennas per sector and up to 5× system bandwidth, LTE-A will require substantial increases in CPRI capacity. In this work, we develop compression methods that exploit the temporal and spectral structure of LTE signals with the goal of achieving high compression with limited impact on end-to-end communication performance. Our contributions include (i) design of a low-complexity compression method for LTE and (ii) validation of this method using an LTE link-level simulation. Our method achieves up to 5× compression for uplink and downlink signals.
Performance Bounds in OFDM Channel Prediction

OFDM channel prediction algorithms have been proposed to combat feedback delay in adaptive OFDM systems. However, no simple closed-form expression that relates prediction mean-square error (MSE) with the design parameters in OFDM channel prediction has been reported in the literature. This paper attempts to fill that gap by deriving simple closed- form asymptotic lower bounds of the MSE for OFDM channel prediction in deterministic doubly-selective fading channels. The bounds relate the best-achievable MSE with the predictor design parameters in a simple manner, thus providing important insight into the design of OFDM channel predictors. We compare the bounds to the deterministic Cramer-Rao bound for parameters consistent with IEEE 802.16e, and show that the asymptotic bounds are reasonably tight for practical scenarios.
A clustering based approach to 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 versus fragility tradeoffs. We validate the perceptual significance of our hash by testing under Stirmark attacks. Finally, we develop randomized clustering algorithms for the purposes of secure image hashing.
Modeling the self-similar behavior of packetized MPEG-4 video using wavelet-based methods

Video streaming has already been very popular on the Internet through services such as news bulletins from different parts of the world and on-demand music-video clips. With rapidly developing wireless technologies, video streaming to mobile devices will be very common. We investigate the self-similar scaling behavior that is present in variable bit rate (VBR) MPEG-4 video. As the usage of video services over packet-based wireless networks increases, new workload models will be necessary to study the quality of service aspects of video traffic. A key finding of our study is that MPEG-4 video encoder output traffic has fractal behavior and this behavior exists regardless of the compression ratio.
Advanced continuous-time filter design in MATLAB

We present an original software for advanced continuous-time filter design. We have written this software in MATLAB as a toolbox which we call AFDESIGN. The AFDESIGN toolbox is based on original numerical procedures rooted in Jacobi elliptic functions. The AFDESIGN toolbox finds a comprehensive set of optimal designs to represent the infinite solution space. Quite opposite, conventional approaches and commercial software packages return only one design, thereby hiding a wealth of alternative filter designs that can be more robust when implemented in hardware. The AFDESIGN toolbox is easy to use, mouse driven, and user friendly to an unexperienced filter designer or practicing engineer.
Designing an embedded video processing camera using a 16-bit microprocessor for surveillance system

We describe the design and implementation of a hybrid intelligent surveillance system consisting of an embedded system and a personal computer (PC)-based system. The embedded system performs some of the image processing tasks and sends the processed data to a PC. The PC tracks persons and recognizes two-person interactions by using a grayscale side-view image sequence captured by a stationary camera. Based on our previous research, we explored the optimum division of tasks between the embedded system and the PC, simulated the embedded system using dataflow models in Ptolemy, and prototyped the embedded systems in real-time hardware and software using a 16-bit CISC microprocessor. This embedded system processes one frame image in 89 ms, which is within three frame-cycle periods for a 30 Hz video system. In addition, the real-time embedded system prototype uses 5.7 Kbytes of program memory, 854 Kbytes of internal data memory and 2 Mbytes external DRAM.
Non-Parametric Impulsive Noise Mitigation in OFDM Systems Using Sparse Bayesian Learning

Additive asynchronous impulsive noise limits communication performance in certain OFDM systems, such as powerline communications, cellular LTE and 802.11n systems. Under additive impulsive noise, the fast Fourier transform (FFT) in the OFDM receiver introduces time-dependence in the subcarrier noise statistics. As a result, complexity of optimal detection becomes exponential in the number of subcarriers. Many previous approaches assume a statistical model of the impulsive noise and use parametric methods in the receiver to mitigate impulsive noise. Parametric methods degrade with increasing model mismatch, and require training and parameter estimation. In this paper, we apply sparse Bayesian learning techniques to estimate and mitigate impulsive noise in OFDM systems without the need for training. We propose two non-parametric iterative algorithms: (1) estimate impulsive noise by its projection onto null and pilot tones so that the OFDM symbol is recovered by subtracting out the impulsive noise estimate; and (2) jointly estimate the OFDM symbol and impulsive noise utilizing information on all tones. In our simulations, the estimators achieve 5dB and 10dB SNR gains in communication performance respectively, as compared to conventional OFDM receivers.
No-reference synthetic image quality assessment using scene statistics

Measuring visual quality, as perceived by human observers, is becoming increasingly important in many applications where humans are the ultimate consumers of visual information. Significant progress has been made for assessing the subjective quality of natural images, such as those taken by optical cameras. Natural Scene Statistics (NSS) is an important tool for no-reference visual quality assessment of natural images, where the reference image is not needed for comparison. In this paper, we take an important step towards using NSS to automate visual quality assessment of photorealistic synthetic scenes typically found in video games and animated movies. Our primary contributions are (1) conducting subjective tests on our publicly available ESPL Synthetic Image Database containing 500 distorted images (20 distorted images for each of the 25 original images) in 1920 × 1080 format, and (2) evaluating the performance of 17 no-reference image quality assessment (IQA) algorithms using synthetic scene statistics. We find that similar to natural scenes, synthetic scene statistics can be successfully used for IQA and certain statistical features are good for certain image distortions.