nils-herrmann/clean_scirepeval huggingface dataset:
A multiresolution approach for all-digital HDTV

The authors investigate the feasibility of a fully digital HDTV (high-definition television) production, processing, coding, and transmission standard that will be a true alternative to analog television. Such a television system is defined as a medium-range technological goal and will have a direct impact on various technologies, including computers, telecommunications, and high-speed VLSI. The authors describe research directions as well as current progress, especially with respect to hierarchical representations and coding methods, nonlinear interpolation techniques, and joint source-channel coding for digital channels. Multiresolution techniques are central to the authors' approach, because they are useful for compression, joint source-channel coding, and compatibility.<<ETX>>
Raking the Cocktail Party

We present the concept of an acoustic rake receiver-a microphone beamformer that uses echoes to improve the noise and interference suppression. The rake idea is well-known in wireless communications; it involves constructively combining different multipath components that arrive at the receiver antennas. Unlike spread-spectrum signals used in wireless communications, speech signals are not orthogonal to their shifts. Therefore, we focus on the spatial structure, rather than the temporal. Instead of explicitly estimating the channel, we create correspondences between early echoes in time and image sources in space. These multiple sources of the desired and the interfering signal offer additional spatial diversity that we can exploit in the beamformer design. We present several “intuitive” and optimal formulations of acoustic rake receivers, and show theoretically and numerically that the rake formulation of the maximum signal-to-interference-and-noise ratio beamformer offers significant performance boosts in terms of noise and interference suppression. Beyond signal-to-noise ratio, we observe gains in terms of the perceptual evaluation of speech quality (PESQ) metric for the speech quality. We accompany the paper by the complete simulation and processing chain written in Python. The code and the sound samples are available online at http://lcav.github.io/AcousticRake Receiver/.
Wave Field coding in the spacetime frequency domain

We present a new method for compressing spatio-temporal audio data for reproduction through wave field synthesis. The data is obtained by sampling the sound field in space at equally-spaced points on a straight line, and transformed into the frequency domain using a spatio-temporal lapped transform. The two-dimensional spectrum is quantized using a psychoacoustic model derived for spatio-temporal frequencies, which estimates the maximum quantization noise power that each frequency can support in order to preserve transparency in the decoded signal. On the decoder side, the inverse lapped transform recovers the spatio-temporal data. In our experimental results, we verified that the bitrate-efficiency can be improved by increasing either the spatial sampling frequency or the spatial resolution of the lapped transform.
The recursive hessian sketch for adaptive filtering

We introduce in this paper the recursive Hessian sketch, a new adaptive filtering algorithm based on sketching the same exponentially weighted least squares problem solved by the recursive least squares algorithm. The algorithm maintains a number of sketches of the inverse autocorrelation matrix and recursively updates them at random intervals. These are in turn used to update the unknown filter estimate. The complexity of the proposed algorithm compares favorably to that of recursive least squares. The convergence properties of this algorithm are studied through extensive numerical experiments. With an appropriate choice or parameters, its convergence speed falls between that of least mean squares and recursive least squares adaptive filters, with less computations than the latter.
Orthonormal finite ridgelet transform for image compression

A finite implementation of the ridgelet transform is presented. The transform is invertible, non-redundant and achieved via fast algorithms. Furthermore we show that this transform is orthogonal hence it allows one to use non-linear approximations for the representation of images. Numerical results on different test images are shown. Those results conform with the theory of the ridgelet transform in the continuous domain-the obtained representation can represent efficiently images with linear singularities. Thus it indicates the potential of the proposed system as a new transform for coding of images.
Exact sampling results for some classes of parametric nonbandlimited 2-D signals

We present sampling results for certain classes of two-dimensional (2-D) signals that are not bandlimited but have a parametric representation with a finite number of degrees of freedom. While there are many such parametric signals, it is often difficult to propose practical sampling schemes; therefore, we will concentrate on those classes for which we are able to give exact sampling algorithms and reconstruction formulas. We analyze in detail a set of 2-D Diracs and extend the results to more complex objects such as lines and polygons. Unlike most multidimensional sampling schemes, the methods we propose perfectly reconstruct such signals from a finite number of samples in the noiseless case. Some of the techniques we use are already encountered in the context of harmonic retrieval and error correction coding. In particular, singular value decomposition (SVD)-based methods and the annihilating filter approach are both explored as inherent parts of the developed algorithms. Potentials and limitations of the algorithms in the noisy case are also pointed out. Applications of our results can be found in astronomical signal processing, image processing, and in some classes of identification problems.
Balanced multiwavelets theory and design

This article deals with multiwavelets, which are a generalization of wavelets in the context of time-varying filter banks and with their applications to signal processing and especially compression. By their inherent structure, multiwavelets are fit for processing multichannel signals. This is the main issue in which we are interested. First, we review material on multiwavelets and their links with multifilter banks and, especially, time-varying filter banks. Then, we have a close look at the problems encountered when using multiwavelets in applications, and we propose new solutions for the design of multiwavelets filter banks by introducing the so-called balanced multiwavelets.
Joint source/channel coding of statistically multiplexed real-time services on packet networks

The interaction of congestion control with the partitioning of source information into components of varying importance for variable-bit-rate packet voice and packet video is investigated. High-priority transport for the more important signal components results in substantially increased objective service quality. Using a Markov chain voice source model with simple PCM speech encoding and a priority queue, simulation results show a signal-to-noise ratio improvement of 45 dB with two priorities over an unprioritized system. Performance is sensitive to the fraction of traffic placed in each priority, and the optimal partition depends on network loss conditions. When this partition is optimized dynamically, quality degrades gracefully over a wide range of load values. Results with DCT encoded speech and video samples show similar behavior. Variations are investigated such as further partition of low-priority information into multiple priorities. A simulation with delay added to represent other network nodes shows general insensitivity to delay of network feedback information. A comparison is made between dropping packets on buffer overflow and timeout based on service requirements.
On the capacity of large Gaussian relay networks

The capacity of a particular large Gaussian relay network is determined in the limit as the number of relays tends to infinity. Upper bounds are derived from cut-set arguments, and lower bounds follow from an argument involving uncoded transmission. It is shown that in cases of interest, upper and lower bounds coincide in the limit as the number of relays tends to infinity. Hence, this paper provides a new example where a simple cut-set upper bound is achievable, and one more example where uncoded transmission achieves optimal performance. The findings are illustrated by geometric interpretations. The techniques developed in this paper are then applied to a sensor network situation. This is a network joint source-channel coding problem, and it is well known that the source-channel separation theorem does not extend to this case. The present paper extends this insight by providing an example where separating source from channel coding does not only lead to suboptimal performance-it leads to an exponential penalty in performance scaling behavior (as a function of the number of nodes). Finally, the techniques developed in this paper are extended to include certain models of ad hoc wireless networks, where a capacity scaling law can be established: When all nodes act purely as relays for a single source-destination pair, capacity grows with the logarithm of the number of nodes.
Which Distributed Averaging Algorithm Should I Choose for my Sensor Network?

Average consensus and gossip algorithms have recently received significant attention, mainly because they constitute simple and robust algorithms for distributed information processing over networks. Inspired by heat diffusion, they compute the average of sensor networks measurements by iterating local averages until a desired level of convergence. Confronted with the diversity of these algorithms, the engineer may be puzzled in his choice for one of them. As an answer to his/her need, we develop precise mathematical metrics, easy to use in practice, to characterize the convergence speed and the cost (time, message passing, energy...) of each of the algorithms. In contrast to other works focusing on time-invariant scenarios, we evaluate these metrics for ergodic time-varying networks. Our study is based on Oseledec's theorem, which gives an almost- sure description of the convergence speed of the algorithms of interest. We further provide upper bounds on the convergence speed. Finally, we use these tools to make some experimental observations illustrating the behavior of the convergence speed with respect to network topology and reliability in both average consensus and gossip algorithms.
Set theoretic compression with an application to image coding

We show that the complete information that is available after an image has been encoded is not just an approximate quantized image version, but a whole set of consistent images that contains the original image by necessity. From this starting point, we develop a set of tools to design a new class of encoders for image compression, based on a set decomposition and recombination of the image features. As an initial validation, we show the results of an experiment where these tools are used to modify the encoding process of block discrete cosine transform (DCT) coding in order to yield less blocking artifacts.
Rotation invariant texture characterization and retrieval using steerable wavelet-domain hidden Markov models

We present a statistical model for characterizing texture images based on wavelet-domain hidden Markov models. With a small number of parameters, the new model captures both the subband marginal distributions and the dependencies across scales and orientations of the wavelet descriptors. Applied to the steerable pyramid, once it is trained for an input texture image, the model can be easily steered to characterize that texture at any other orientation. Furthermore, after a diagonalization operation, we obtain a rotation-invariant model of the texture image. We also propose a fast algorithm to approximate the Kullback-Leibler distance between two wavelet-domain hidden Markov models. We demonstrate the effectiveness of the new texture models in retrieval experiments with large image databases, where significant improvements are shown.
Privacy-preserving function computation by exploitation of friendships in social networks

We study the problem of privacy-preserving computation of functions of data that belong to users in a social network under the assumption that users are willing to share their private data with trusted friends in the network. We demonstrate that such trust relationships can be exploited to significantly improve the tradeoff between the privacy of users' data and the accuracy of the computation. Under a one-hop trust model we design an algorithm for partitioning the users into circles of trust and develop a differentially private scheme for computing the global function using results of local computations within each circle. We quantify the improvement in the privacy-accuracy tradeoff of our scheme with respect to other mechanisms that do not exploit inter-user trust. We verify the efficiency of our algorithm by implementing it on social networks with up to one million nodes. Applications of our method include surveys, elections, and recommendation systems.
Outlier removal for improved source estimation in atmospheric inverse problems

Estimation of the quantities of harmful substances emitted into the atmosphere is one of the main challenges in modern environmental sciences. In most of the cases, this estimation requires solving a linear inverse problem. A key difficulty in evaluating the performance of any algorithm to solve this linear inverse problem is that the ground truth is typically unknown. In this paper we show that the noise encountered in this linear inverse problem is non-Gaussian. Next, we develop an algorithm to deal with the strong outliers present in the measurements. Finally, we test our approach on three different experiments: a simple synthetic experiment, a controlled real-world experiment, and real data from the Fukushima nuclear accident.
Transform coding using adaptive bases and quantization

This paper considers the problem of universal transform coding based on estimating the Karhunen-Loeve transform from quantized data. The use of quantized data in the estimation allows the encoder and decoder to maintain the same state without any side information. A theorem is presented that proves, under certain conditions, that consistent estimation of all the required moments is possible from uniformly scalar quantized data regardless of the quantization coarseness. This consistent estimation requires the solution of nonlinear equations. Very simple approximations that avoid these nonlinear equations are used to develop a practical adaptive coding technique. Promising experimental results obtained with this method are presented.
Sampling of communication systems with bandwidth expansion

Many communication systems are bandwidth-expanding: the transmitted signal occupies a bandwidth larger than the symbol rate. The sampling theorems of Kotelnikov, Shannon, Nyquist et al. discussed by Unser  2000) shows that in order to represent a bandlimited signal, it is necessary to sample at what is popularly referred to as the Shannon or Nyquist rate. However, in many systems, the required sampling rate is very high and expensive to implement. We show that it is possible to get suboptimal performance by sampling close to the symbol rate of the signal, using well-studied algorithmic components. This work is based on previous results on sampling for some classes of non-bandlimited signals. We extend these sampling results to the case when there is noise. In our exposition, we use ultra wideband (UWB) signals as an example of how our framework can be applied.
A sampling theorem for the Radon transform of finite complexity objects

We present sampling results for certain classes of 2-D signals that are not bandlimited, but have a parametric representation with a finite number of degrees of freedom, such as 2-D Diracs, polygons and bilevel signals with piecewise polynomial boundaries. As opposed to standard multidimensional sampling schemes, the proposed methods exploit the properties of the Radon transform of such signals. In particular, we demonstrate that by using an appropriate sampling kernel, one can perfectly reconstruct the signal from a finite set of samples of its Radon transform, and thus significantly reduce a computational load. The novel approach we present in the paper, offers practical algorithmic implementation and is potentially applicable to a large class of two-dimensional signals.
Video multicast in (large) local area networks

We consider the problem of distributing high-quality video signals over IP multicast in large local area networks (LANs), under realtime delay constraints, and with software-only processing. In a large LAN (such as the network of a university campus, or the network of a large company), the source of channel heterogeneity that the video communications system must cope with is not that of different bandwidth constraints available to each receiver, but it is essentially variations in the available CPU power that each receiver has in order to decode the incoming signal. We propose a new architecture for a video multicast system, present the design of the different components of this system, and show results obtained in a real implementation. Our feeds consist of video encoded at about 3 Mbits/sec and 16 frames/sec, capable of tolerating the loss of about 300 Kbits/sec worth of data. Decoding is performed on Linux PCs, and the quality of our reconstructed signals degrades gracefully with the speed of the CPU on which the receiver runs.
A theory of multirate filter banks

Multirate filter banks produce multiple output signals by filtering and subsampling a single input signal, or conversely, generate a single output by upsampling and interpolating multiple inputs. Two of their main applications are subband coders for speech processing and transmultiplexers for telecommunications. Below, we derive a theoretical framework for the analysis, synthesis, and computational complexity of multirate filter banks. The use of matrix notation leads to basic results derived from properties of linear algebra. Using rank and determinant of filter matrices, it is shown how to obtain aliasing/ crosstalk-free reconstruction, and when perfect reconstruction is possible. The synthesis of filters for filter banks is also explored, three design methods are presented, and finally, the computational complexity is considered.
On the accuracy of point localisation in a circular camera-array

Although many advances have been made in light-field and camera-array image processing, there is still a lack of thorough analysis of the localisation accuracy of different multi-camera systems. By considering the problem from a frame-quantisation perspective, we are able to quantify the point localisation error of circular camera configurations. Specifically, we obtain closed form expressions bounding the localisation error in terms of the parameters describing the acquisition setup. These theoretical results are independent of the localisation algorithm and thus provide fundamental limits on performance. Furthermore, the new frame-quantisation perspective is general enough to be extended to more complex camera configurations.
Fast 2-D discrete cosine transform

A fast radix-2 two dimensional discrete cosine transform (DCT) is presented. First, the mapping into a 2-D discrete Fourier transform (DFT) of a real signal is improved. Then an usual polynomial transform approach is used in order to map the 2-D DFT into a reduced size 2-D DFT and one dimensional odd DFT's. Finally, optimized odd DFT algorithms for real signals are developped. All together, a reduction of more than 50% in the number of multiplications and a comparable amount of additions is obtained in comparison to other algorithm.
Deterministic analysis of oversampled A/D conversion and decoding improvement based on consistent estimates

Discusses the deterministic analysis of oversampled A/D conversion (ADC), the properties derivable from such an analysis, and the consequences on reconstruction using nonlinear decoding. Given a band-limited input X producing a quantized version C, the authors consider the set of all input signals that are band-limited and produce C. They call any element of this set a consistent estimate of X. Regardless of the type of encoder (simple, predictive, or noise-shaping), they show that this set is convex, and as a consequence, any nonconsistent estimate can be improved. They also show that the classical linear decoding estimates are not necessarily consistent. Numerical tests performed on simple ADC, single-loop, and multiloop /spl Sigma//spl Delta/ modulation show that consistent estimates yield a mean square error (MSE) that decreases asymptotically with the oversampling ratio faster than the linear decoding MSE by approximately 3 dB/octave. This implies an asymptotic MSE of the order of /spl Oscr/(R/sup /spl minus/(2n+2/)) instead of /spl Oscr/(R/sup /spl minus/(2n+1/)) in linear decoding, where R is the oversampling ratio and n the order of the modulator. Methods of improvement of nonconsistent estimates based on the deterministic knowledge of the quantized signal are proposed for simple ADC, predictive ADC, single-loop, and multiloop /spl Sigma//spl Delta/ modulation.
Compressed sensing with probabilistic measurements: A group testing solution

Detection of defective members of large populations has been widely studied in the statistics community under the name “group testing”, a problem which dates back to World War II when it was suggested for syphilis screening. There, the main interest is to identify a small number of infected people among a large population using collective samples. In viral epidemics, one way to acquire collective samples is by sending agents inside the population. While in classical group testing, it is assumed that the sampling procedure is fully known to the reconstruction algorithm, in this work we assume that the decoder possesses only partial knowledge about the sampling process. This assumption is justified by observing the fact that in a viral sickness, there is a chance that an agent remains healthy despite having contact with an infected person. Therefore, the reconstruction method has to cope with two different types of uncertainty; namely, identification of the infected populapteioopnle and the partially unknown sampling procedure. In this work, by using a natural probabilistic model for “viral infections”, we design non-adaptive sampling procedures that allow successful identification of the infected population with overwhelming probability 1 − o(1). We propose both probabilistic and explicit design procedures that require a “small” number of agents to single out the infected individuals. More precisely, for a contamination probability p, the number of agents required by the probabilistic and explicit designs for identification of up to k infected members is bounded by m = O(k2(log n)/p2) and m = O(k2 (log2 n)/p2), respectively. In both cases, a simple decoder is able to successfully identify the infected population in time O(mn).