nils-herrmann/clean_scirepeval huggingface dataset:
Enhancing local - Transmitting less - Improving global

Super-resolving a natural image is an ill-posed problem. The classical approach is based on the registration and subsequent interpolation of a given set of low-resolution images. However, achieving satisfactory results typically requires the combination of a large number of them. Such an approach would be impractical over heterogeneous rate-constrained wireless networks due to the associated communication cost and limited data available. In this paper, we present an approach for local image enhancement following the finite rate of innovation sampling framework, and motivate its application to the super-resolution problem over heterogeneous networks. Local estimates can be exchanged among the nodes of the network in order to regularize the super-resolution problem while, at the same time, reduce data exchange.
Super-Resolution From Unregistered and Totally Aliased Signals Using Subspace Methods

In many applications, the sampling frequency is limited by the physical characteristics of the components: the pixel pitch, the rate of the analog-to-digital (AID) converter, etc. A low- pass filter is usually applied before the sampling operation to avoid aliasing. However, when multiple copies are available, it is possible to use the information that is inherently present in the aliasing to reconstruct a higher resolution signal. If the different copies have unknown relative offsets, this is a nonlinear problem in the offsets and the signal coefficients. They are not easily separable in the set of equations describing the super-resolution problem. Thus, we perform joint registration and reconstruction from multiple unregistered sets of samples. We give a mathematical formulation for the problem when there are M sets of N samples of a signal that is described by L expansion coefficients. We prove that the solution of the registration and reconstruction problem is generically unique if MN ges L + M - 1. We describe two subspace-based methods to compute this solution. Their complexity is analyzed, and some heuristic methods are proposed. Finally, some numerical simulation results on one- and two-dimensional signals are given to show the performance of these methods.
Perfect reconstruction FIR filter banks: lapped transforms, pseudo QMFs and paraunitary matrices

Perfect-reconstruction FIR (finite-impulse-response) filter banks are analyzed in both the z-transform and time domains. A condition for equal-length analysis and synthesis filters is given in terms of orthogonality constraints on overlapping parts of the filters. If the further condition that the filters themselves form an orthonormal basis set is met, one obtains a paraunitary (in the z-transform domain) or unitary solution (in the time domain). For the restricted length case of L=2N, solutions are shown to exist (like the lapped orthogonal transform or the pseudo-QMF modulated filters) that allow perfect reconstruction and lend themselves to a fast algorithm implementation. Therefore, there is a large class of computationally efficient perfect-reconstruction FIR filter banks where analysis and synthesis have an identical frequency behavior.<<ETX>>
Sampling and exact reconstruction of bandlimited signals with additive shot noise

In this correspondence, we consider sampling continuous-time periodic bandlimited signals which contain additive shot noise. The classical sampling scheme does not perfectly recover these particular nonbandlimited signals but only reconstructs a lowpass filtered approximation. By modeling the shot noise as a stream of Dirac pulses, we first show that the sum of a bandlimited signal with a stream of Dirac pulses falls into the class of signals that contain a finite rate of innovation, that is, a finite number of degrees of freedom. Second, by taking into account the degrees of freedom of the bandlimited signal in the sampling and reconstruction scheme developed previously for streams of Dirac pulses, we derive a sampling and perfect reconstruction scheme for the bandlimited signal with additive shot noise.
Rate-distortion problem for physics based distributed sensing [temperature measurement]

We consider the rate-distortion problem for sensing the continuous space-time physical temperature in a circular ring on which a heat source is applied over space and time, and which is allowed to cool by radiation or convection. The heat source is modelled as a continuous space-time stochastic process which is bandlimited over space and time. The temperature field is the result of a certain continuous space-time convolution of the heat source with the Green's function corresponding to the heat equation, which is space and time invariant. The temperature field is sampled at uniform spatial locations by a set of sensors and it has to be reconstructed at a base station. The goal is to minimize the mean-square-error per second, for a given number of nats per second, assuming ideal communication channels between sensors and base station. We find a) the centralized R/sup c/(D) function of the temperature field, where the base station can optimally encode all the space-time samples jointly. Then, we obtain b) the R/sup s-i/(D) function, where each sensor, independently, encodes its samples optimally over time, and c) the R/sup st-i/(D) function, where each sensor is constrained to encode also independently over time. We also study two distributed prediction-based approaches: a) with perfect feedback from the base station, where temporal prediction is performed at the base station and each sensor performs differential encoding; and b) without feedback, where each sensor locally performs temporal prediction.
Computation-distortion characteristics of JPEG encoding and decoding

A distortion-computation function D(C) is defined as the minimum expected distortion in computing some quantity-using an algorithm from a predefined set of algorithms-while using no more than C computational units. When the computational problem is to encode at rate R, this gives slices of a computation-rate-distortion surface. This framework is used in the analysis of a family of JPEG coders that use output-pruned DCT calculations in place of some full DCT calculations. For encoding the Lena image at 0.5 bits/pixel, this yields a 30% reduction in complexity while lowering the PSNR by only 0.4 dB. The decoding complexity can be similarly reduced.
Communication using phantoms: covert channels in the Internet

We consider the problem of determining the transport capacity of point-to-point and broadcast channels implemented on top of a network that enforces max-min fair bandwidth allocations in its routers. Our main finding is that the use of inefficient codes to represent data that is intended to be used solely for network control operations (such as routing, sequencing, etc.), gives rise to the unintended creation of a covert channel. Sources can encode some information for their destinations into network control bits (on top of the standard method of encoding data into payload bits), by means of a mechanism which we refer to as the generation of "phantom" packets. Although phantoms provide only a marginal bandwidth increase, they could have potentially vast reaching implications in terms of security issues.
The plenacoustic function, sampling and reconstruction

We study the spatialization of the sound field in a room, in particular the evolution of room impulse responses as a function of their spatial positions. The presented technique allows us to characterize completely the sound field in any arbitrary location if the sound field is known in a certain finite number of positions. Existing techniques usually make use of room models to recreate the sound field present at some point in the space. Our technique simply starts from the measurements of impulse responses at a finite number of positions, and, with this information, the total sound field can be recreated. An analytical solution of the problem is given for different cases of spaces. Further, we determine the number of microphones and the spacing between them needed to reconstruct the sound field perfectly up to a certain temporal frequency. The optimal sampling pattern for the microphone positions is given. Applications are also discussed.
Reproducible research in signal processing

What should we do to raise the quality of signal processing publications to an even higher level? We believe it to be crucial to maintain the precision in describing our work in publications, ensured through a high-quality reviewing process. We also believe that if the experiments are performed on a large data set, the algorithm is compared to the state-of-the-art methods, the code and/or data are well documented and available online, we will all benefit and make it easier to build upon each other's work. It is a clear win-win situation for our community: we will have access to more and more algorithms and can spend time inventing new things rather than recreating existing ones.
Vector quantization of image subbands: a survey

Subband and wavelet decompositions are powerful tools in image coding because of their decorrelating effects on image pixels, the concentration of energy in a few coefficients, their multirate/multiresolution framework, and their frequency splitting, which allows for efficient coding matched to the statistics of each frequency band and to the characteristics of the human visual system. Vector quantization (VQ) provides a means of converting the decomposed signal into bits in a manner that takes advantage of remaining inter and intraband correlation as well as of the more flexible partitions of higher dimensional vector spaces. Since 1988, a growing body of research has examined the use of VQ for subband/wavelet transform coefficients. We present a survey of these methods.
Valuable Detours: Least-Cost Anypath Routing

In many networks, it is less costly to transmit a packet to any node in a set of neighbors than to one specific neighbor. This observation was previously exploited by opportunistic routing protocols by using single-path routing metrics to assign to each node a group of candidate relays for a particular destination. This paper addresses the least-cost anypath routing (LCAR) problem: how to assign a set of candidate relays at each node for a given destination such that the expected cost of forwarding a packet to the destination is minimized. The key is the following tradeoff: On one hand, increasing the number of candidate relays decreases the forwarding cost, but on the other, it increases the likelihood of “veering” away from the shortest-path route. Prior proposals based on single-path routing metrics or geographic coordinates do not explicitly consider this tradeoff and, as a result, do not always make optimal choices. The LCAR algorithm and its framework are general and can be applied to a variety of networks and cost models. We show how LCAR can incorporate different aspects of underlying coordination protocols, for example a link-layer protocol that randomly selects which receiving node will forward a packet, or the possibility that multiple nodes mistakenly forward a packet. In either case, the LCAR algorithm finds the optimal choice of candidate relays that takes into account these properties of the link layer. Finally, we apply LCAR to low-power, low-rate wireless communication and introduce a new wireless link-layer technique to decrease energy transmission costs in conjunction with anypath routing. Simulations show significant reductions in transmission cost to opportunistic routing using single-path metrics. Furthermore, LCAR routes are more robust and stable than those based on single-path distances due to the integrative nature of the LCAR's route cost metric.
Raking echoes in the time domain

The geometry of room acoustics is such that the reverberant signal can be seen as the same waveform emitted from multiple locations. In analogy with the rake receiver from wireless communications, we propose several beamforming strategies that exploit, rather than suppress, this additional spatio-temporal diversity. Unlike earlier work in the frequency domain, time domain designs allow to shape the impulse response of the beamformer. In particular, we can control perceptually relevant parameters, such as the amount of early echoes or the length of the beamformer response. Relying on the knowledge of the image sources positions, we derive different optimal beamformers. Leveraging perceptual cues, we show how to improve interference and noise reduction without degrading the perceptual quality. The designs are validated through simulation. Using early echoes is shown to strictly improve the signal to interference and noise ratio. Code and speech samples are available online at http:// lcav.epfl.ch/Robin_Scheibler.
Shapes From Pixels

Continuous-domain visual signals are usually captured as discrete (digital) images. This operation is not invertible in general, in the sense that the continuous-domain signal cannot be exactly reconstructed based on the discrete image, unless it satisfies certain constraints (e.g., bandlimitedness). In this paper, we study the problem of recovering shape images with smooth boundaries from a set of samples. Thus, the reconstructed image is constrained to regenerate the same samples (consistency), as well as forming a shape (bilevel) image. We initially formulate the reconstruction technique by minimizing the shape perimeter over the set of consistent binary shapes. Next, we relax the non-convex shape constraint to transform the problem into minimizing the total variation over consistent non-negative-valued images. We also introduce a requirement (called reducibility) that guarantees equivalence between the two problems. We illustrate that the reducibility property effectively sets a requirement on the minimum sampling density. We also evaluate the performance of the relaxed alternative in various numerical experiments.
Perfect reconstruction filter banks with rational sampling factors

An open problem, namely, how to construct perfect reconstruction filter banks with rational sampling factors, is solved. Such filter banks have N branches, each one having a sampling factor of p i/qi, and their sum equals one. In this way, the well-known theory of filter banks with uniform band splitting is extended to allow for nonuniform divisions of the spectrum. This can be very useful in the analysis of speech and music. The theory relies on two transforms. The first transform leads to uniform filter banks having polyphase components as individual filters. The other results in a uniform filter bank containing shifted versions of same filters. This, in turn, introduces dependencies in design, and is left for future work. As an illustration, several design examples for the (2/3, 1/3) case are given. Filter banks are then classified according to the possible ways in which they can be built. It is shown that some cases cannot be solved even with ideal filters (with real coefficients)
Opportunistic Sampling for Joint Population Size and Density Estimation

Consider a set of probes, called “agents”, who sample, based on opportunistic contacts, a population moving between a set of discrete locations. An example of such agents are Bluetooth probes that sample the visible Bluetooth devices in a population. Based on the obtained measurements, we construct a parametric statistical model to jointly estimate the total population size (e.g., the number of visible Bluetooth devices) and their spatial density. We evaluate the performance of our estimators by using Bluetooth traces obtained during an open-air event and Wi-Fi traces obtained on a university campus.
Design of multidimensional filter banks for non-separable sampling

Results from the design of multidimensional filter banks with arbitrary sampling patterns are presented, with emphasis on the particular cases of quincunx and hexagonal sampling. Conditions for what is and what is not achievable given specific design constraints (such as perfectly diamond-shaped filters, linear phase, and paraunitariness) are stated. Useful cascade structures are given, and the issue of completeness is addressed. The hexagonal case is analyzed, and a structure allowing perfect reconstruction while yielding linear phase and paraunitary filters is proposed. Two applications are considered: obtaining directional subband decomposition with hexagonal filters, and decomposition of interlaced and progressively scanned television with quincunx ones.<<ETX>>
Orthogonal time-varying filter banks and wavelets

The construction of time-varying orthogonal filter banks is considered. It is shown that implementing an orthogonal finite impulse response filter bank over a finite signal segment involves finding a set of orthogonal boundary filters, and that by carrying out a Gram-Schmidt orthogonalization procedure boundary filters are generated that necessarily remain localized in the region of the boundary. A complete constructive characterization of such boundaries is given for two-channel finite impulse response filter banks. These boundary constructions allow changing the topology of orthogonal subband trees at will, by growing or pruning branches at any time. The boundary filter case can be further generalized to give overlapping transition filters when changing between orthogonal structures. If the time-varying filter banks are used in an iterated scheme, they converge to continuous-time bases, much as in the non-time-varying case.<<ETX>>
Towards Generalized FRI Sampling With an Application to Source Resolution in Radioastronomy

It is a classic problem to estimate continuous-time sparse signals, like point sources in a direction-of-arrival problem, or pulses in a time-of-flight measurement. The earliest occurrence is the estimation of sinusoids in time series using Prony's method. This is at the root of a substantial line of work on high resolution spectral estimation. The estimation of continuous-time sparse signals from discrete-time samples is the goal of the sampling theory for finite rate of innovation (FRI) signals. Both spectral estimation and FRI sampling usually assume uniform sampling. But not all measurements are obtained uniformly, as exemplified by a concrete radioastronomy problem we set out to solve. Thus, we develop the theory and algorithm to reconstruct sparse signals, typically sum of sinusoids, from nonuniform samples. We achieve this by identifying a linear transformation that relates the unknown uniform samples of sinusoids to the given measurements. These uniform samples are known to satisfy the annihilation equations. A valid solution is then obtained by solving a constrained minimization such that the reconstructed signal is consistent with the given measurements and satisfies the annihilation constraint. Thanks to this new approach, we unify a variety of FRI-based methods. We demonstrate the versatility and robustness of the proposed approach with five FRI reconstruction problems, namely Dirac reconstructions with irregular time or Fourier domain samples, FRI curve reconstructions, Dirac reconstructions on the sphere, and point source reconstructions in radioastronomy. The proposed algorithm improves substantially over state-of-the-art methods and is able to reconstruct point sources accurately from irregularly sampled Fourier measurements under severe noise conditions.
Adaptive filtering in sub-bands

Adaptive filtering in subbands, that is adapting several short filters in parallel on subsampled frequency subbands, is an attractive alternative to implementing a long adaptive FIR (finite-impulse response) filter, due to its computational efficiency and faster convergence. The subsampling process introduces aliased versions of the original signals; it is shown how to annihilate these by adequate adaptive cross terms. The computational complexity is improved nearly proportionally to the number of subbands, and the conference speed is improved as well, making this scheme attractive for applications like acoustic echo cancellation.<<ETX>>
EchoSLAM: Simultaneous localization and mapping with acoustic echoes

We address the problem of jointly localizing a robot in an unknown room and estimating the room geometry from echoes. Unlike earlier work using echoes, we assume a completely autonomous setup with (near) collocated microphone and the acoustic source. We first introduce a simple, easy to analyze estimator, and prove that the sequence of room and trajectory estimates converges to the true values. Next, we approach the problem from a Bayesian point of view, and propose a more general solution which does not require any assumptions on motion and measurement model of the robot. In addition to theoretical analysis, we validate both estimators numerically.
Image reconstruction in the gigavision camera

Recently we have proposed a new image device called the gigavision camera. The main feature of this camera is that the pixels have a binary response. The response function of a gigavision sensor is non-linear and similar to a logarithmic function, which makes the camera suitable for high dynamic range imaging. Since the sensor can detect a single photon, the camera is very sensitive and can be used for night vision and astronomical imaging. One important aspect of the gigavision camera is how to estimate the light intensity through binary observations. We model the light intensity field as 2D piecewise constant and use Maximum Penalized Likelihood Estimation (MPLE) to recover it. Dynamic programming is used to solve the optimization problem. Due to the complex computation of dynamic programming, greedy algorithm and pruning quadtrees are proposed. They show acceptable reconstruction performance with low computational complexity. Experimental results with synthesized images and real images taken by a single-photon avalanche diode (SPAD) camera are given.
Estimation of Sparse MIMO Channels with Common Support

We consider the problem of estimating sparse communication channels in the MIMO context. In small to medium bandwidth communications, as in the current standards for OFDM and CDMA communication systems (with bandwidth up to 20 MHz), such channels are individually sparse and at the same time share a common support set. Since the underlying physical channels are inherently continuous-time, we propose a parametric sparse estimation technique based on finite rate of innovation (FRI) principles. Parametric estimation is especially relevant to MIMO communications as it allows for a robust estimation and concise description of the channels. The core of the algorithm is a generalization of conventional spectral estimation methods to multiple input signals with common support. We show the application of our technique for channel estimation in OFDM (uniformly/contiguous DFT pilots) and CDMA downlink (Walsh-Hadamard coded schemes). In the presence of additive white Gaussian noise, theoretical lower bounds on the estimation of sparse common support (SCS) channel parameters in Rayleigh fading conditions are derived. Finally, an analytical spatial channel model is derived, and simulations on this model in the OFDM setting show the symbol error rate (SER) is reduced by a factor 2 (0 dB of SNR) to 5 (high SNR) compared to standard non-parametric methods - e.g. lowpass interpolation.