nils-herrmann/clean_scirepeval huggingface dataset:
Framing pyramids

Burt and Adelson (1983) introduced the Laplacian pyramid (LP) as a multiresolution representation for images. We study the LP using the frame theory, and this reveals that the usual reconstruction is suboptimal. We show that the LP with orthogonal filters is a tight frame, and thus, the optimal linear reconstruction using the dual frame operator has a simple structure that is symmetric with the forward transform. In more general cases, we propose an efficient filterbank (FB) for the reconstruction of the LP using projection that leads to a proved improvement over the usual method in the presence of noise. Setting up the LP as an oversampled FB, we offer a complete parameterization of all synthesis FBs that provide perfect reconstruction for the LP. Finally, we consider the situation where the LP scheme is iterated and derive the continuous-domain frames associated with the LP.
New results on multidimensional filter banks and wavelets

New results on multidimensional filter banks and wavelets are presented. Emphasis is placed on the two-dimensional case with sampling by 2 in each dimension. It is shown how, by using an appropriate cascade structure, nonseparable filters being both orthogonal and linear phase can be obtained, a construction not possible when one-dimensional systems are used separately. The condition for having at least one zero of the lowpass filter at all aliasing frequencies (necessary for convergence) is given. A first design example of two-dimensional orthonormal wavelets with symmetries is presented, together with a demonstration of continuity.<<ETX>>
Sampling and reconstruction of time-varying atmospheric emissions

We study the spatio-temporal sampling of physical fields representing the dispersion of a substance in the atmosphere. We consider the following setup: N sensors are deployed at ground level and measure the concentration of a particular substance, while M smokestacks are located in the same area and emit a time-varying amount of the substance. To recover the emission rates of the smokestacks with a limited number of spatio-temporal samples, we consider time varying emissions rates lying in two specific low-dimensional subspaces. We propose efficient algorithms and sufficient conditions to recover the emission rates of the smokestacks from the local measurements collected by the sensor network.
Locating mobile nodes with EASE: learning efficient routes from encounter histories alone

Routing in large-scale mobile ad hoc networks is challenging because all the nodes are potentially moving. Geographic routing can partially alleviate this problem, as nodes can make local routing decisions based solely on the destinations' geographic coordinates. However, geographic routing still requires an efficient location service, i.e., a distributed database recording the location of every destination node. Devising efficient, scalable, and robust location services has received considerable attention in recent years. The main purpose of this paper is to show that node mobility can be exploited to disseminate destination location information without incurring any communication overhead. We achieve this by letting each node maintain a local database of the time and location of its last encounter with every other node in the network. This database is consulted by packets to obtain estimates of their destination's current location. As a packet travels towards its destination, it is able to successively refine an estimate of the destination's precise location, because node mobility has "diffused" estimates of that location. We define and analyze a very simple algorithm called EASE (Exponential Age Search) and show that in a model where Theta(n) nodes perform independent random walks on a square lattice of size n, the length of the routes computed by EASE are of the same order as the distance between the source and destination even for very large n. Therefore, without disseminating any explicit location information, the length of EASE routes are within a constant factor of routes obtained with perfect information. We discuss refinements of the EASE algorithm and evaluate it through extensive simulations. We discuss general conditions such that the mobility diffusion effect leads to efficient routes without an explicit location service. In practical settings, where these conditions may not always be met, we believe that the mobility diffusion effect can complement existing location services and enhance their robustness and scalability
Applications of short space-time fourier analysis in digital acoustics

This paper presents a signal processing tool for analyzing and manipulating digitized acoustic wave fields, based on a spatio-temporal extension of the time-frequency representation space. The emphasis is on wave fields acquired with a 1-D linear array of equidistant microphones (representing the spatial samples), but the basic formulation is valid for the three dimensions of space. We start by defining a spatio-temporal extension of the short time Fourier transform, in both continuous and discrete space and time, and then focus on applications of the proposed representation. In particular, we show that acoustic scenes with multiple sources can benefit from the use of space-frequency analysis in applications such as source separation (spatial filtering) and spatial audio coding (wave field coding). The experiments suggest that there is a spatial window size for which the performance of filtering and coding is optimal.
Networked Slepian-Wolf: theory, algorithms, and scaling laws

Consider a set of correlated sources located at the nodes of a network, and a set of sinks that are the destinations for some of the sources. The minimization of cost functions which are the product of a function of the rate and a function of the path weight is considered, for both the data-gathering scenario, which is relevant in sensor networks, and general traffic matrices, relevant for general networks. The minimization is achieved by jointly optimizing a) the transmission structure, which is shown to consist in general of a superposition of trees, and b) the rate allocation across the source nodes, which is done by Slepian-Wolf coding. The overall minimization can be achieved in two concatenated steps. First, the optimal transmission structure is found, which in general amounts to finding a Steiner tree, and second, the optimal rate allocation is obtained by solving an optimization problem with cost weights determined by the given optimal transmission structure, and with linear constraints given by the Slepian-Wolf rate region. For the case of data gathering, the optimal transmission structure is fully characterized and a closed-form solution for the optimal rate allocation is provided. For the general case of an arbitrary traffic matrix, the problem of finding the optimal transmission structure is NP-complete. For large networks, in some simplified scenarios, the total costs associated with Slepian-Wolf coding and explicit communication (conditional encoding based on explicitly communicated side information) are compared. Finally, the design of decentralized algorithms for the optimal rate allocation is analyzed.
Error-Rate Characteristics of Oversampled Analog-to-Digital Conversion

Accuracy of simple analog-to-digital conversion depends on both resolution of discretization in amplitude and resolution of discretization in time. For implementation convenience, high conversion accuracy is attained by refining the discretization in time using oversampling. It is commonly believed that oversampling adversely impacts rate-distortion properties of the conversion, since the bit rate, B, increases linearly with oversampling, resulting in a slow error decay in the bit rate, on the order of O(1/B). We demonstrate that the information obtained in the process of oversampled analog-to-digital conversion can easily be encoded in a manner which requires only a logarithmic increase of the bit rate with redundancy, achieving an exponential error decay in the bit rate.
Plenacoustic function on the circle with application to HRTF interpolation

The sampling of a sound field using circular microphone arrays is studied. We give a new representation of the sound field sampled on such an array using the two-dimensional Fourier transform. We then show how we can reconstruct the sound field at all possible positions on the array from a finite number of measurements. We give the necessary azimuthal angular sampling frequency necessary to reconstruct the data up to some temporal frequency. This new technique is applied to interpolate head related transfer functions (HRTFs) where we also derive an angular sampling theorem.
Distributed successive approximation coding using broadcast advantage: The two-encoder case

Traditional distributed source coding rarely considers the possible link between separate encoders. However, the broadcast nature of wireless communication in sensor networks provides a free gossip mechanism which can be used to simplify encoding/decoding and reduce transmission power. Using this broadcast advantage, we present a new two-encoder scheme which imitates the ping-pong game and has a successive approximation structure. For the quadratic Gaussian case, we prove that this scheme is successively refinable on the {sum-rate, distortion pair} surface, which is characterized by the rate-distortion region of the distributed two-encoder source coding. A potential energy saving over conventional distributed coding is also illustrated. This ping-pong distributed coding idea can be extended to the multiple encoder case and provides the theoretical foundation for a new class of distributed image coding method in wireless scenarios.
On simple oversampled A/D conversion in L2(IR)

The accuracy of oversampled analog-to-digital (A/D) conversion, the dependence of accuracy on the sampling interval /spl tau/ and on the bit rate R are characteristics fundamental to A/D conversion but not completely understood. These characteristics are studied for oversampled A/D conversion of band-limited signals in L/sup 2/ (R). We show that the digital sequence obtained in the process of oversampled A/D conversion describes the corresponding analog signal with an error which tends to zero as /spl tau//sup 2/ in energy, provided that the quantization threshold crossings of the signal constitute a sequence of stable sampling in the respective space of band-limited functions. Further, we show that the sequence of quantized samples can be represented in a manner which requires only a logarithmic increase in the bit rate with the sampling frequency, R=O(|log/spl tau/|), and hence that the error of oversampled A/D conversion actually exhibits an exponential decay in the bit rate as the sampling interval tends to zero.
Near-field adaptive beamforming and source localization in the spacetime frequency domain

We revisit the topics of near-field adaptive beamforming and source localization following an alternative approach based on a spatio-temporal spectral representation of the acoustic wave field. With the proposed method, the wave field is expressed as a separable combination of the signal and spatial components that characterize the various sources in the acoustic scene. This allows beamforming operations such as beam steering and sidelobe canceling to be translated into a two-dimensional (2D) sampling problem, where the sampling kernels are derived according to a parametric model representing the 2D spectral pattern generated in the presence of a source. Conversely, the spectral pattern can be estimated from an arbitrary input through the use of parametric spectral estimation techniques, providing a novel solution to the near-field source localization problem.
Vector quantization view of /spl Sigma//spl Delta/ modulation

We analyze the behavior of a single-loop /spl Sigma//spl Delta/ modulator using a vector quantization (VQ) approach. We extract the encoder-decoder structure typical of VQ, existing in a /spl Sigma//spl Delta/ modulator. The study of the encoding part gives the intrinsic behavior of the modulator. Bounds to the intrinsic performance of the /spl Sigma//spl Delta/ modulator can be derived through this approach in the oversampling situation, assuming periodicity of the bandlimited input signals.<<ETX>>
Event-driven video coding for outdoor wireless monitoring cameras

Reducing communication cost is crucial for outdoor wireless monitoring cameras which are constrained by limited energy budgets. From event detection point of view, traditional video coding schemes such as H.264 are inefficient as they ignore the “meaning” of video content and thus waste many bits to convey irrelevant information. To take advantage of the powerful computing resource on cameras, we propose a novel event-driven video coding scheme. Unlike previous approach that attempts to find anomalous image frame with potential events, we propose to detect salient regions in each image and transmit the image fragments marked with saliency to the receiver. This scheme rarely drops an event as it transmits all image fragments with potential events, and also requires no training procedure. The experimental results show that it performs substantially better than conventional video coding schemes for outdoor monitoring task.
Near-Optimal Sensor Placement for Linear Inverse Problems

A classic problem is the estimation of a set of parameters from measurements collected by only a few sensors. The number of sensors is often limited by physical or economical constraints and their placement is of fundamental importance to obtain accurate estimates. Unfortunately, the selection of the optimal sensor locations is intrinsically combinatorial and the available approximation algorithms are not guaranteed to generate good solutions in all cases of interest. We propose FrameSense, a greedy algorithm for the selection of optimal sensor locations. The core cost function of the algorithm is the frame potential, a scalar property of matrices that measures the orthogonality of its rows. Notably, FrameSense is the first algorithm that is near-optimal in terms of mean square error, meaning that its solution is always guaranteed to be close to the optimal one. Moreover, we show with an extensive set of numerical experiments that FrameSense achieves state-of-the-art performance while having the lowest computational cost, when compared to other greedy methods.
Perfect transmultiplexers

The mathematical equivalence of transmultiplexers and sub-band coders with reconstruction of the original signals is shown. Critical time sampling in sub-band coders corresponds to critical frequency sampling in transmultiplexers. In both cases, non-ideal band-pass filters are sufficient to reconstruct the original signals without aliasing (in sub-band coders) and without crosstalk (in transmultiplexers). The equivalence between the two systems allows the use of results from sub-band coders (like the QMF and pseudo-QMF concepts) for the design of transmultiplexers. The price to be paid is a synchronous up- and down-sampling between transmitter and receiver, as well as a good channel characteristic. Simulation results are given for the non-ideal case. All results are obtained through a matrix analysis of the filter bank problem.
The gigavision camera

We propose a new image device called gigavision camera. The main differences between a conventional and a gigavision camera are that the pixels of the gigavision camera are binary and orders of magnitude smaller. A gigavision camera can be built using standard memory chip technology, where each memory bit is designed to be light sensitive. A conventional gray level image can be obtained from the binary gigavision image by low-pass filtering and sampling. The main advantage of the gigavision camera is that its response is non-linear and similar to a logarithmic function, which makes it suitable for acquiring high dynamic range scenes. The larger the number of binary pixels considered, the higher the dynamic range of the gigavision camera will be. In addition, the binary sensor of the gigavision camera can be combined with a lens array in order to realize an extremely thin camera. Due to the small size of the pixels, this design does not require deconvolution techniques typical of similar systems based on conventional sensors.
Distributed Compression in Acoustic Sensor Networks using Oversampled A/D Conversion

We address the problem of distributed compression in acoustic sensor networks. A typical scenario consists of a set of microphones that record a sound source located at some unknown position. The goal then is to convey the corresponding audio signals to a central base station for processing. We thus aim at designing efficient distributed communication schemes that minimize the overall bit-rate needed in order to achieve a given reconstruction accuracy. In this paper, we show how the a priori knowledge of the system's geometry can be beneficially used in order to lower the transmission rates. We propose a distributed coding scheme for simple synthetic sources. These results are then applied to more general signals by means of oversampled analog-to-digital conversion. Simulation results confirm the effectiveness of our approach
Pyramidal directional filter banks and curvelets

A flexible multiscale and directional representation for images is proposed. The scheme combines directional filter banks with the Laplacian pyramid to provide a sparse representation for two-dimensional piecewise smooth signals resembling images. The underlying expansion is a frame and can be designed to be a tight frame. Pyramidal directional filter banks provide an effective method to implement the digital curvelet transform. The regularity issue of the iterated filters in the directional filter bank is examined.
Resolution enhancement of images using wavelet transform extrema extrapolation

One problem of image interpolation refers to magnifying a small image without loss in image clarity. We propose a wavelet based method which estimates the higher resolution information needed to sharpen the image. This method extrapolates the wavelet transform of the higher resolution based on the evolution of the wavelet transform extrema across the scales. By identifying three constraints that the higher resolution information needs to obey, we enhance the reconstructed image through alternating projections onto the sets defined by these constraints.
Wavelet extrema and zero-crossings representations: properties and consistent reconstruction

Properties of nonsubsampled FIR filter banks, used for wavelet extrema and zero-crossings representations, are investigated. Conditions under which iterated nonsubsampled filter banks implement frame or tight frame operators are given and relations with continuous time domain are established. Algorithms for consistent reconstruction of signals from wavelet extrema or zero crossings representations are proposed. These algorithms are characterized by a low computational complexity and a simple implementation. It is also shown that the wavelet extrema representation of a signal and the wavelet zero-crossings representation of its difference provide equivalent information on the signal.<<ETX>>
Relax and unfold: Microphone localization with Euclidean distance matrices

Recent methods for microphone position calibration work with sound sources at a priori unknown locations. This is convenient for ad hoc arrays, as it requires little additional infrastructure. We propose a flexible localization algorithm by first recognizing the problem as an instance of multidimensional unfolding (MDU) - a classical problem in Euclidean geometry and psychometrics - and then solving the MDU as a special case of Euclidean distance matrix (EDM) completion. We solve the EDM completion using a semidefinite relaxation. In contrast to existing methods, the semidefinite formulation allows us to elegantly handle missing pairwise distance information, but also to incorporate various prior information about the distances between the pairs of microphones or sources, bounds on these distances, or ordinal information such as "microphones 1 and 2 are more apart than microphones 1 and 15". The intuition that this should improve the localization performance is justified by numerical experiments.
Spatial adaptive wavelet thresholding for image denoising

Wavelet thresholding with uniform threshold has shown some success in denoising. For images, we propose that this can be improved by adjusting thresholds spatially, based on the rationale that detailed regions such as edges and textures tolerate some noise but not blurring, whereas smooth regions tolerate blurring but not noise. The proposed algorithm is based on multiscale edge detection and image segmentation and then thresholding the coefficients of different regions with adaptive thresholds.
Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance

We present a statistical view of the texture retrieval problem by combining the two related tasks, namely feature extraction (FE) and similarity measurement (SM), into a joint modeling and classification scheme. We show that using a consistent estimator of texture model parameters for the FE step followed by computing the Kullback-Leibler distance (KLD) between estimated models for the SM step is asymptotically optimal in term of retrieval error probability. The statistical scheme leads to a new wavelet-based texture retrieval method that is based on the accurate modeling of the marginal distribution of wavelet coefficients using generalized Gaussian density (GGD) and on the existence a closed form for the KLD between GGDs. The proposed method provides greater accuracy and flexibility in capturing texture information, while its simplified form has a close resemblance with the existing methods which uses energy distribution in the frequency domain to identify textures. Experimental results on a database of 640 texture images indicate that the new method significantly improves retrieval rates, e.g., from 65% to 77%, compared with traditional approaches, while it retains comparable levels of computational complexity.
Outlier Modeling in Image Matching

We address the question of how to characterize the outliers that may appear when matching two views of the same scene. The match is performed by comparing the difference of the two views at a pixel level aiming at a better registration of the images. When using digital photographs as input, we notice that an outlier is often a region that has been occluded, an object that suddenly appears in one of the images, or a region that undergoes an unexpected motion. By assuming that the error in pixel intensity generated by the outlier is similar to an error generated by comparing two random regions in the scene, we can build a model for the outliers based on the content of the two views. We illustrate our model by solving a pose estimation problem: the goal is to compute the camera motion between two views. The matching is expressed as a mixture of inliers versus outliers, and defines a function to minimize for improving the pose estimation. Our model has two benefits: First, it delivers a probability for each pixel to belong to the outliers. Second, our tests show that the method is substantially more robust than traditional robust estimators (M-estimators) used in image stitching applications, with only a slightly higher computational complexity.