nils-herrmann/clean_scirepeval huggingface dataset:
Probabilistic 3-D motion estimation for rolling shutter video rectification from visual and inertial measurements

Video acquired by handheld CMOS cameras may suffer from rolling shutter artifacts. Rolling shutter artifacts, which are due to the rows in the image sensor array being exposed sequentially from top to bottom, increase with the speed of the relative motion between the scene and camera. To rectify these artifacts, one needs to recover the projection parameters for each row. In this paper, we propose a probabilistic method to estimate 3-D camera rotation by using video and inertial measurements on the handheld platform, such as a smart phone. Our contributions are (1) an efficient sensor fusion algorithm using an extended Kalman filter, and (2) a quality assessment method using vanishing point detection. Experiments indicate that the proposed sensor fusion algorithm produces a more accurate orientation estimate and better rectifies rolling shutter artifacts.
An achievable performance upper bound for discrete multitone equalization

Discrete multitone is a popular discrete Fourier transform based implementation of multicarrier modulation for wireline communications. The paper examines the performance of a complex-tap filter bank structure for channel equalization in discrete multitone modulation systems. The structure under study passes the received signal through a number of logical paths. Each path takes care of one data carrying subcarrier. More specifically, each path cascades a finite impulse response frequency selective equalizer and a Goertzel filter computing a single point discrete Fourier transform. The delay on each logical path is individually optimized for best performance. In the presented simulations testing the achievable bit rate on an asymmetric digital subscriber line transceiver, the filter bank appears to benchmark the bit rate performance among existing discrete multitone equalization methods. An iterative training procedure, which depends on the second-order statistics of the input and output sequences, is proposed to show the achievability of performance.
Real-Time Testbed for Diversity in Powerline and Wireless Smart Grid Communications

Two-way communication is a key feature in a smart grid. It is enabled by either powerline communication or wireless communication technologies. Utilizing both technologies can potentially enhance communication reliability, and many diversity combining schemes have been proposed. In this paper, we propose a flexible real-time testbed to evaluate diversity combining schemes over physical channels. The testbed provides essential parts of physical layers on which both powerline and wireless communications operate. The contributions of this paper are 1) design and implementation of a real-time testbed for diversity of simultaneous powerline and wireless communications, 2) release of the setup information and complete source code for the testbed, and 3) performance evaluation of maximal ratio combining (MRC) on the testbed. As initial results, we show that performance of MRC from measurements obtained on the testbed over physical channels is very close to that in simulations in various test cases under a controlled laboratory environment.
Families of Smith form decompositions to simplify multidimensional filter bank design

Imposing structure on the Smith form of an (integer) periodicity matrix N=U/spl Lambda/V leads to efficient m-D DFT implementations. For resampling matrices, i.e. non-singular rational matrices, the authors introduce Smith form decomposition algorithms to generate /spl Lambda/ matrices whose diagonal elements exhibit minimum variance and U matrices with minimum norm. Such structure simplifies non-uniform m-D filter bank design.<<ETX>>
A factor-graph approach to joint OFDM channel estimation and decoding in impulsive noise channels

We propose a factor-graph-based approach to joint channel, impulse, symbol and bit estimation (JCISB) of LDPC-coded orthogonal frequency division multiplexing (OFDM) systems in impulsive noise environments. Impulsive noise arises in many modern wireless and wireline communication systems, such as cellular LTE and powerline communications, due to uncoordinated interference that is much stronger than thermal noise. Our receiver merges prior knowledge of the impulsive noise models with the recently proposed “generalized approximate message passing” (GAMP) algorithm, and soft-input soft-output decoding through the sum-product framework. Unlike the prior work, we explicitly consider channel estimation in the problem formulation. For N subcarriers, the resulting receiver has a complexity of O(N logN), comparable to a typical DFT receiver. Numerical results indicate that the proposed receiver outperforms all prior impulsive noise OFDM decoders with improvements that reach 13dB when compared to the commonly used DFT receiver.
Adaptive experimental design for drug combinations

Drug cocktails formed by mixing multiple drugs at various doses provide more effective cures than single-drug treatments. However, drugs interact in highly nonlinear ways making the determination of the optimal combination a difficult task. The response surface of the drug cocktail has to be estimated through expensive and time-consuming experimentation. Previous research focused on the use of space-exploratory heuristics such as genetic algorithms to guide the search for optimal combinations. While being more efficient than random sampling, these methods require a considerable amount of experiments to converge to good solutions. In this paper, we propose to use an information-theoretic active learning approach under the Bayesian framework of Gaussian processes to adaptively choose what experiments to perform based on current data points. We show that our approach is able to reduce the number of required data points significantly.
Patch-based image deconvolution via joint modeling of sparse priors

Image deconvolution aims to recover an image that has been degraded by a linear operation such as blurring during image acquisition. Deconvolution based on maximum a-posteriori (MAP) estimation requires the global prior probability of the original image. Conventional methods usually model the image priors by uniformly characterizing the statistical properties of either some forward measurements of images or the representation coefficients in frames, neglecting the local image statistics. In this paper, we adopt local sparse representation in image deconvolution. Our contributions include proposing (1) a joint model of natural images combining sparse representation of image patches and sparse gradient priors, and (2) an efficient iterative algorithm to infer the MAP estimate of image deconvolution using the proposed model. Experiments indicate that the proposed method can recover the original image with high peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) index compared with state-of-the-art methods.
Wordlength optimization with complexity-and-distortion measure and its application to broadband wireless demodulator design

Many digital signal processing algorithms are first developed in floating point and later mapped into fixed point for digital hardware implementation. During this mapping, wordlengths are searched to minimize total hardware cost and maximize system performance. Complexity and distortion measures have been separately researched for optimum wordlength selection. This paper proposes a complexity-and-distortion measure (CDM) method that combines these two measures. The CDM method trades off these two measures using a weighting factor. The proposed method is applied to wordlength design of a fixed broadband wireless demodulator. For this case study, the proposed method finds the optimal solution in one-third the time that exhaustive search takes. The contributions of this paper are: (1) a generalization of search methods based on complexity or distortion measures; (2) a framework of automatic wordlength optimization; and (3) a wireless demodulator case study.
Upper bounds on MIMO channel capacity with channel Frobenius norm constraints

The motivation of this paper is to find the class of channels that provides the largest capacity with both the transmit power constraint and channel Frobenius norm constraints. We study both the point-to-point case and the broadcast case. For point-to-point MIMO channels, the optimal channels must have T* equal singular values, where T* is dependent on the available transmit power and the channel Frobenius norm. This result agrees with the previous work by Chiurtu et al. For multiuser broadcast channels, we obtain an upper bound on the sum capacity. We also show that the bound is asymptotically tight for high SNR when all user channels have the same Frobenius norm constraint and N/sub t/ /spl ges/ KN/sub r/, where N/sub t/ and N/sub r/ are the numbers of transmit and receive antennas and K is the number of users.
Effect of channel estimation error on bit rate performance of time domain equalizers

Channel equalization plays a key role in achieving high bit rates in wireline multicarrier systems. Some VDSL systems and all standardized multicarrier ADSL systems employ time domain equalization (channel shortening) and frequency domain equalization in channel equalization. In this paper, we analyze the impact of imperfect channel estimates on the bit rate performance of four time domain equalization methods. We derive a closed-form expression for the bit rate loss due to channel estimation error. We simulate the sensitivity in bit rate performance using first-generation downstream ADSL transmission. In simulation, the minimum intersymbol interference and minimum mean square error methods are relatively insensitive to channel estimation errors vs. minimum delay spread and maximum shortening signal-to-noise ratio methods.
Efficient matrix multiplication methods to implement a near-optimum channel shortening method for discrete multitone transceivers

The minimum intersymbol interference (min-ISI) method yields time-domain equalizer (TEQ) designs for discrete multitone (DMT) modulation transceivers that are close to channel capacity. For eight standard ADSL channels, the min-ISI design method reaches within 1% of the matched filter bound at the TEQ output. However, the min-ISI method relies several computationally expensive matrix multiplications. We develop low-complexity algorithms for these multiplications to allow for real-time implementation of the min-ISI method on programmable digital signal processors.
Low Complexity User Selection Algorithms for Multiuser MIMO Systems with Block Diagonalization

Block diagonalization (BD) is a preceding technique that eliminates inter-user interference in downlink multiuser multiple-input multiple-output (MIMO) systems. With the assumptions that all users have the same number of receive antennas and utilize all receive antennas when scheduled for transmission, the number of simultaneously supportable users with BD is limited by the ratio of the number of basestation transmit antennas to the number of user receive antennas. In a downlink MIMO system with a large number of users, the basestation may select a subset of users to serve in order to maximize the total throughput The brute-force search for the optimal user set, however, is computationally prohibitive. We propose two low-complexity suboptimal user selection algorithms for multiuser MIMO systems with BD. Both algorithms aim to select a subset of users such that the total throughput is nearly maximized. The first user selection algorithm greedily maximizes the total throughput, whereas the criterion of the second algorithm is based on the channel energy. We show that both algorithms have linear complexity in the total number of users and achieve around 95% of the total throughput of the complete search method in simulations
Infinite length results for channel shortening equalizers

Time-domain equalization is crucial in reducing state dimension in maximum likelihood sequence estimation, and intercarrier and intersymbol interference in 802.11a and ADSL multicarrier systems. A time-domain equalizer, or TEQ, which is a finite impulse response (FIR) filter, placed in cascade with the channel produces an effective impulse response of /spl nu/+1 samples that is shorter than the channel impulse response. The paper analyzes the two families of TEQ design methods amenable to cost-effective real-time implementation: minimum mean squared error (MMSE) and maximum shortening SNR (MSSNR) methods. For infinite length TEQs, we prove that MMSE target impulse responses are symmetric and have all /spl nu/ zeros on the unit circle, and MSSNR TEQs have /spl nu/ of their zeros on the unit circle. Consequently, finite-length MMSE and MSSNR TEQs eventually yield increasing bit error rates (for broadcast systems) or decreasing bit rates (for point-to-point systems that allow bit allocation) with increasing filter length.
Sonar data compression using non-uniform quantization and noise shaping

Sonar sensor arrays potentially produce huge amounts of data to be recorded or transmitted over a telemetry system. Compression can reduce the required storage or transmission bandwidth, or allow a larger or higher fidelity array. We use a dataset for a sonar array receiving acoustic communication signals from a transmitter in a lake test. We compress the received signals to evaluate the effect of compression on performance. Based on analysis of the dataset, we use non-uniform quantization with a Laplace distribution along with noise-shaped feedback coding. We demonstrate that this sonar data can be compressed from 16-bit to five-bit values with little or no change in performance using our technique.
Adaptive resource allocation in multiuser OFDM systems with proportional rate constraints

Multiuser orthogonal frequency division multiplexing (MU-OFDM) is a promising technique for achieving high downlink capacities in future cellular and wireless local area network (LAN) systems. The sum capacity of MU-OFDM is maximized when each subchannel is assigned to the user with the best channel-to-noise ratio for that subchannel, with power subsequently distributed by water-filling. However, fairness among the users cannot generally be achieved with such a scheme. In this paper, a set of proportional fairness constraints is imposed to assure that each user can achieve a required data rate, as in a system with quality of service guarantees. Since the optimal solution to the constrained fairness problem is extremely computationally complex to obtain, a low-complexity suboptimal algorithm that separates subchannel allocation and power allocation is proposed. In the proposed algorithm, subchannel allocation is first performed by assuming an equal power distribution. An optimal power allocation algorithm then maximizes the sum capacity while maintaining proportional fairness. The proposed algorithm is shown to achieve about 95% of the optimal capacity in a two-user system, while reducing the complexity from exponential to linear in the number of subchannels. It is also shown that with the proposed resource allocation algorithm, the sum capacity is distributed more fairly and flexibly among users than the sum capacity maximization method.
In-Camera Automation of Photographic Composition Rules

At the time of image acquisition, professional photographers apply many rules of thumb to improve the composition of their photographs. This paper develops a joint optical-digital processing framework for automating composition rules during image acquisition for photographs with one main subject. Within the framework, we automate three photographic composition rules: repositioning the main subject, making the main subject more prominent, and making objects that merge with the main subject less prominent. The idea is to provide to the user alternate pictures obtained by applying photographic composition rules in addition to the original picture taken by the user. The proposed algorithms do not depend on prior knowledge of the indoor/outdoor setting or scene content. The proposed algorithms are also designed to be amenable to software implementation on fixed-point programmable digital signal processors available in digital still cameras.
Integrating analysis, simulation, and implementation tools in electronic courseware for teaching signal processing

A typical path in learning digital signal processing begins at the theoretical end and progresses toward the practical constraints imposed by implementation in hardware or software. On this path, the student would learn how to convert mathematical theory into algorithms and then algorithms into efficient implementations. In this paper, we first summarize the electronic courseware we have already developed in Mathematica, MATLAB, and Ptolemy to teach DSP theory, algorithms, and implementation, respectively. Then, we discuss ways to integrate our efforts to help students discover the connections between these topics.
Spectral Efficiency Bounds for Interference-Limited SVD-MIMO Cellular Communication Systems

The ergodic spectral efficiency (SE) in interference-limited multiple-input multiple-output (MIMO) downlink cellular systems is characterized based on stochastic geometry. A single user is served by using singular value decomposition precoding and combining. By approximating the expectations of the channel eigenvalues, we derive upper and lower bounds on the ergodic SE. The obtained upper bound is the best possible system-level performance of any MIMO strategy in non-cooperative cellular networks. We validate our analytical results through simulation. We also conjecture that there exists the optimal number of streams being proportional to the pathloss exponent.
Automated design of two-dimensional rational decimation systems

The paper gives an automated procedure to design rational decimation compression systems that resample two-dimensional bandpass signals at their Nyquist rates. Our procedure takes a sketch of the desired bandpass shape in the frequency domain, circumscribes it with a parallelogram of minimal area, and linearly maps the minimal enclosing parallelogram onto one period of the frequency domain. From the rational matrix that performs the linear mapping, we compute the parameters of the upsampler, filter, and downsampler in the compression system. The compression system only has linear components.<<ETX>>
Cyclic spectral analysis of power line noise in the 3–200 kHz band

Narrowband OFDM Power Line Communication (NB-OFDM PLC) systems are a key component of current and future smart grids. NB-OFDM PLC systems enable next-generation smart metering, distributed control, and monitoring applications over existing power delivery infrastructure. It has been shown that the performance of these systems is severely limited by impulsive, non-Gaussian additive noise. A substantial component of this noise has time-periodic statistics (i.e. it is cyclostationary) synchronous to the AC mains cycle. In this work, we analyze the cyclic structure of power line noise observed in a G3 PLC system operating in the CENELEC 3-148.5 kHz band. Our contributions include: (i) the characterization of noise measurements in several urban usage environments, (ii) the development of a cyclic bit loading method for G3, and (iii) the quantification of its throughput gains over measured noise. Through this analysis, we confirm strong cyclostationarity in power lines and identify several sources of the cyclic noise.
Fusing interferometric radar and laser altimeter data to estimate surface topography and vegetation heights

Interferometric synthetic aperture radar (INSAR) and laser altimeter (LIDAR) systems are both widely used for mapping topography. INSAR can map extended areas but accuracies are limited over vegetated regions, primarily because the observations are not measurements of true surface topography. The measurements correspond to a height above the true surface that depends on both the sensor and the vegetation. Conversely, topography from LIDAR is very accurate, but coverage is limited to smaller regions. We demonstrate how these technologies can be used synergistically. First, we determine surface elevations and vegetation heights from dual-baseline INSAR data by inverting an INSAR scattering model. We then combine sparse LIDAR observations with the INSAR inversion results to improve the estimates of ground elevations and vegetation heights. This is accomplished via a multiresolution Kalman Filter that provides both the estimates and a measure of their uncertainty at each location. Combining data from the two sensors provides estimates that are more accurate than those obtained from INSAR alone yet have dense, extensive coverage, which is difficult to obtain with LIDAR. Contributions of this work include 1) combining physical modeling with multiscale estimation to accommodate nonlinear measurement-state relationships and 2) improving estimates of ground elevations and vegetation heights for remote sensing applications.
Investigating signal processing theory with Mathematica

The authors present extensions to the computer algebra and multimedia environment Mathematica to enable students to investigate signal processing theory. Students interact with the extensions at two levels. The lower level is a set of Mathematica programs called the signal processing packages (SPPs) that implement signals and systems not found in the core of Mathematica. The higher level is a set of interactive multimedia notebook tutorials on analog filters, convolution, Fourier analysis, and the z-transform. Other notebooks have been written to help students to evaluate their knowledge of digital filter design and convolution.<<ETX>>
Optimization of signal processing algorithms

We optimize implementations of one-dimensional and multidimensional signal processing algorithms by rewriting subexpressions according to a set of algebraic identities. We encode the algebraic identities as conditional rules, and program hill climbing and simulated annealing search techniques to apply the rules. Both of these search techniques avoid an exponential explosion in memory usage because they only keep a single state in memory instead of building the entire tree of possible equivalent forms. We compare the effectiveness of these search techniques in optimizing implementations of one-dimensional multirate signal processing algorithms. Our prototype environment is written in Mathematica.
Fast unbiased echo canceller update during ADSL transmission

This paper presents an efficient method for updating echo canceller (EC) coefficients in asymmetric digital subscriber line transceivers during transmission that removes the dependency of updates on the far-end signal. The new method improves on the frequency-domain EC architecture by Ho et al. (1996), in that it converges faster, improves the stability to perturbation, and is not prone to bias from pilot tones. The new EC updating system architecture is presented for both the central office and remote terminal systems and is fully compliant with the ITU-T G.DMT standard.