nils-herrmann/clean_scirepeval huggingface dataset:
Data Compression and Harmonic Analysis

In this paper we review some recent interactions between harmonic analysis and data compression. The story goes back of course to Shannon's R(D) theory in the case of Gaussian stationary processes, which says that transforming into a Fourier basis followed by block coding gives an optimal lossy compression technique; practical developments like transform-based image compression have been inspired by this result. In this paper we also discuss connections perhaps less familiar to the information theory community, growing out of the field of harmonic analysis. Recent harmonic analysis constructions, such as wavelet transforms and Gabor transforms, are essentially optimal transforms for transform coding in certain settings. Some of these transforms are under consideration for future compression standards. We discuss some of the lessons of harmonic analysis in this century. Typically, the problems and achievements of this field have involved goals that were not obviously related to practical data compression, and have used a language not immediately accessible to outsiders. Nevertheless, through an extensive generalization of what Shannon called the "sampling theorem", harmonic analysis has succeeded in developing new forms of functional representation which turn out to have significant data compression interpretations. We explain why harmonic analysis has interacted with data compression, and we describe some interesting recent ideas in the field that may affect data compression in the future.
Rate distortion behavior of threshold-based nonlinear approximations

Many transform coders use a type of nonlinear approximation that selects all coefficients with magnitudes above threshold, encodes their positions, and and quantizes their values. This kind of two-stage scheme is very effective at low rates. We derive an upper bound on the operational rate distortion function of such threshold-based nonlinear approximations. The bound is applied to the spike process, which is a generic model for sparse transform coefficients, and to a Gaussian mixture process that is a better model for wavelet coefficients in image transform codes. The results exhibit the same change in distortion decay between low and high rates which is typical for such image coders.
Near-Optimal Thermal Monitoring Framework for Many-Core Systems-on-Chip

Chip designers place on-chip thermal sensors to measure local temperatures, thus preventing thermal runaway situations in many-core processing architectures. However, the quality of the thermal reconstruction is directly dependent on the number of placed sensors, which should be minimized, while guaranteeing full detection of all the worst case temperature gradient. In this paper, we present an entire framework for the thermal management of complex many-core architectures, such that we can precisely recover the thermal distribution from a minimal number of sensors. The proposed sensor placement algorithm is guaranteed to reduce the impact of noisy measurements on the reconstructed thermal distribution. We achieve significant improvements compared to the state of the art, in terms of both computational complexity and reconstruction precision. For example, if we consider a 64 cores systems-on-chip with 64 noisy sensors (σ2 = 4), we achieve an average reconstruction error of 1:5°C, that is less than half of what previous state-of-the-art methods achieve. We also study the practical limits of the proposed method and show that we do not need realistic workloads to learn the model and efficiently place the sensors. In fact, we show that the reconstruction error is not significantly increased if we randomly generate the power-traces of the components or if we have just a part of the correct workload.
Oversampled filter banks

Perfect reconstruction oversampled filter banks are equivalent to a particular class of frames in l/sup 2/(Z). These frames are the subject of this paper. First, the necessary and sufficient conditions of a filter bank for implementing a frame or a tight frame expansion are established, as well as a necessary and sufficient condition for perfect reconstruction using FIR filters after an FIR analysis. Complete parameterizations of oversampled filter banks satisfying these conditions are given. Further, we study the condition under which the frame dual to the frame associated with an FIR filter bank is also FIR and give a parameterization of a class of filter banks satisfying this property. Then, we focus on non-subsampled filter banks. Non-subsampled filter banks implement transforms similar to continuous-time transforms and allow for very flexible design. We investigate the relations of these filter banks to continuous-time filtering and illustrate the design flexibility by giving a procedure for designing maximally flat two-channel filter banks that yield highly regular wavelets with a given number of vanishing moments.
Multi-channel low-frequency room equalization using perceptually motivated constrained optimization

We consider the problem of multiple-loudspeaker low-frequency room equalization for a wide listening area, where the equalized loudspeaker is helped using the remaining ones. Using a spatial discretization of the listening area, we formulate the problem as a multipoint error minimization between desired and synthesized magnitude frequency responses. The desired response and cost function are formulated with a goal of capturing the room's spectral power profile, and penalizing strong resonances. Considering physical and psychoacoustical observations, we argue for the use of gain-limited, short, and well-localized equalization filters, with an additional delay for loudspeakers that help the equalized one. We propose a convex optimization framework for computing room equalization filters, where the mentioned filter requirements are incorporated as convex constraints. We verify the effectiveness of our equalization approach through simulations.
Perfect reconstruction FIR filter banks: some properties and factorizations

Perfect reconstruction finite impulse-response (FIR) filter banks are analyzed both in the z-transform and time domains, showing the alternatives between designs in the two domains. Various classes of perfect reconstruction schemes are indicated, and relations between previously known systems are given. Windowed modulated filter banks with low computational complexity and perfect reconstruction are shown. New factorizations of polyphase filter matrices leading in particular to linear-phase filters, are given. The computational complexity and the architecture of the new structures are indicated.
Efficient routing with small buffers in dense networks

The analysis and design of routing algorithms for finite buffer networks requires solving the associated queue network problem which is known to be hard. We propose alternative and more accurate approximation models to the usual Jackson's theorem that give more insight into the effect of routing algorithms on the queue size distributions. Using the proposed approximation models, we analyze and design routing algorithms that minimize overflow losses in grid networks with finite buffers and different communication patterns, namely uniform communication and data gathering. We show that the buffer size required to achieve the maximum possible rate decreases as the network size increases. Motivated by the insight gained in grid networks, we apply the same principles to the design of routing algorithms for random networks with finite buffers that minimize overflow losses. We show that this requires adequately combining shortest path tree routing and traveling salesman routing. Our results show that such specially designed routing algorithms increase the transmitted rate for a given loss probability up to almost three times, on average, with respect to the usual shortest path tree routing.
Channel estimation and synchronization with sub-Nyquist sampling and application to ultra-wideband systems

We consider the problem of low-complexity channel estimation and timing in digital ultra-wideband receivers. We extend some of our recent results on sampling of certain classes of parametric non-bandlimited signals and develop a frequency domain framework that yields high-resolution estimates of all relevant channel parameters by sampling a received signal below the traditional nyquist rate. In particular, we show that the minimum required sampling rate in an UWB receiver is determined by the so-called innovation rate, which corresponds to the number of degrees of freedom of the received UWB signal. Our framework allows for faster acquisition compared to current digital solutions and potentially reduces power consumption and complexity of digital UWB receivers significantly. It is particularly suitable in applications such as ranging or positioning and can also be used for identification of more realistic UWB channel models, where different propagation paths undergo different frequency-selective mitigation.
Sampling Continuous-Time Sparse Signals: A Frequency-Domain Perspective

We address the problem of sampling and reconstruction of sparse signals with finite rate of innovation. We derive general conditions under which perfect reconstruction is possible for sampling kernels satisfying Strang-Fix conditions. Previous results on the subject consider two particular cases; when the kernel is able to reproduce (complex) exponentials, or when it has the polynomial reproduction property. In this paper, we extend such analysis to the case where both properties could be found in the sampling kernel and show that the former two situations can be regarded as special cases. As a result of our analysis, we provide general conditions under which perfect recovery in the noiseless case is possible. In practice, a given sampling kernel might not satisfy Strang-Fix conditions. When dealing with arbitrary sampling kernels, we propose a unified view for sampling and reconstruction in the frequency domain. Our formulation generalizes previous approaches and provides new insights for devising optimal reconstruction schemes. We also propose a novel algorithm for denoising treating the problem as a particular instance of structured low-rank approximation. Finally, we provide some numerical experiments and a comparison between different state-of-the-art methods showing the improved estimation performance of the proposed approach.
Texture similarity measurement using Kullback-Leibler distance on wavelet subbands

The focus of this work is on using texture information for searching, browsing and retrieving images from a large database. In the wavelet approaches, texture is characterized by its energy distribution in the decomposed subbands. However it is unclear on how to define similarity functions on extracted features; usually simple norm-based distances together with heuristic normalization are employed. We develop a novel wavelet-based texture retrieval method that is based on the modeling of the marginal distribution of wavelet coefficients using the generalized Gaussian density (GGD) and a closed form Kullback-Leibler distance between the GGDs. The proposed method provides greater accuracy and flexibility in capturing texture information while its simplified form has close resemblance with existing methods. Experimental results indicate that the new method significantly improves retrieval rates, e.g. from 65% to 77%, against traditional approaches while it has comparable levels of computational complexity.
Video multicast over fair queueing networks

We consider the problem of video multicast over networks that enforce fair bandwidth allocations in the routers. State-of-the-art layered IP multicast systems perform well for moderately sized sessions, but suffer from three basic problems that prevent their massive deployment: (a) lack of fairness among different multicast sessions, (b) lack of fairness to competing TCP flows, and (c) high complexity requirements to scale up to potentially large numbers of users and sessions. In this work we present an entirely different approach to the design of these systems, in which sources obliviously inject packets into the network (disregarding congestion), whereas routers obliviously drop the amount of bandwidth that exceeds the fair share of a flow (disregarding packet contents). We model our system as a broadcast channel, with receivers connected to the source via erasure channels of different capacities, and we design a video coder to operate in this environment. Our results suggest that a combination of fair queueing routers and appropriate coding is indeed able to overcome certain drawbacks of current IP multicast systems. Furthermore, we also find that such systems would benefit significantly from being able to re-encode the video signal at internal nodes of the multicast tree.
Rate-Constrained Collaborative Noise Reduction for Wireless Hearing Aids

Hearing aids are electronic, battery-operated sensing devices which aim at compensating various kinds of hearing impairments. Recent advances in low-power electronics coupled with progresses made in digital signal processing offer the potential for substantial improvements over state-of-the-art systems. Nevertheless, efficient noise reduction in complex listening scenarios remains a challenging task, partly due to the limited number of microphones that can be integrated on such devices. We investigate the noise reduction capability of hearing instruments that may exchange data by means of a rate-constrained wireless link and thus benefit from the signals recorded at both ears of the user. We provide the necessary theoretical results to analyze this collaboration mechanism under two different coding strategies. The first approach takes full benefit of the binaural correlation, while the second neglects it, since binaural statistics are difficult to estimate in a practical setting. The gain achieved by collaborating hearing aids as a function of the communication bit rate is then characterized, both in a monaural and a binaural configuration. The corresponding optimal rate allocation strategies are computed in closed form. While the analytical derivation is limited to a simple acoustic scenario, the latter is shown to capture many of the features of the general problem. In particular, it is observed that the loss incurred by coding schemes which do not consider the binaural correlation is rather negligible in a very noisy environment. Finally, numerical results obtained using real measurements corroborate the potential of our approach in a realistic scenario.
The Beauty of the Commons: Optimal Load Sharing by Base Station Hopping in Wireless Sensor Networks

In wireless sensor networks (WSNs), the base station (BS) is a critical sensor node whose failure causes severe data losses. Deploying multiple fixed BSs improves the robustness, yet requires all BSs to be installed with large batteries and large energy-harvesting devices due to the high energy consumption of BSs. In this paper, we propose a scheme to coordinate the multiple deployed BSs such that the energy supplies required by individual BSs can be substantially reduced. In this scheme, only one BS is selected to be active at a time and the other BSs act as regular sensor nodes. We first present the basic architecture of our system, including how we keep the network running with only one active BS and how we manage the handover of the role of the active BS. Then, we propose an algorithm for adaptively selecting the active BS under the spatial and temporal variations of energy resources. This algorithm is simple to implement but is also asymptotically optimal under mild conditions. Finally, by running simulations and real experiments on an outdoor testbed, we verify that the proposed scheme is energy-efficient, has low communication overhead and reacts rapidly to network changes.
Perfect reconstruction filter banks with rational sampling rate changes

The authors present a general, direct method for designing perfect reconstruction filter banks with rational sampling rate changes. Such filter banks have N branches, each one having a sampling factor of p/sub i//q/sub i/ and their sum equal to one. A design example showing the advantage of using the direct over the indirect method is given. Due to recent results pointing to the relationship between filter banks and wavelet theory, the regularity question is addressed as well, and a regular filter is shown for a dilation factor of 3/2.<<ETX>>
Significance tree image coding using balanced multiwavelets

Biorthogonal wavelets have been used with great success in most of the recent transform image coders. By using the new balanced multiwavelets, one can now easily design fully orthogonal linear phase FIR transform schemes. The aim of our work is to assess whether the added orthogonality yields a performance gain compared to traditional biorthogonal transforms. As comparison platform we use the well-known SPIHT codec, which is based on the significance tree quantization (STQ) principle. Without any particular fine-tuning the multiwavelet codec performs within 0.5 dB of SPIHT. A closer inspection shows however that it is hard to improve on this, therefore re-establishing the rule of thumb that strict orthogonality is not a key factor in image transform coding. More details can be obtained on the [WEB] at http://lcavwww.epfl.ch/~weidmann/mwcoder.
A new robust and efficient estimator for ill-conditioned linear inverse problems with outliers

Solving a linear inverse problem may include difficulties such as the presence of outliers and a mixing matrix with a large condition number. In such cases a regularized robust estimator is needed. We propose a new-type regularized robust estimator that is simultaneously highly robust against outliers, highly efficient in the presence of purely Gaussian noise, and also stable when the mixing matrix has a large condition number. We also propose an algorithm to compute the estimates, based on a regularized iterative reweighted least squares algorithm. A basic and a fast version of the algorithm are given. Finally, we test the performance of the proposed approach using numerical experiments and compare it with other estimators. Our estimator provides superior robustness, even up to 40% of outliers, while at the same time performing quite close to the optimal maximum likelihood estimator in the outlier-free case.
Cyclic convolution of real sequences: Hartley versus Fourier and new schemes

Recently, new fast transforms (such as the discrete Hartley transform in particular) have been proposed which are best suited for the computation of cyclic convolution of real sequences. Two approaches using Fourier or Hartley transforms are first compared, showing that the recently proposed FFT algorithms for real data present a lower arithmetic complexity than the corresponding DHT-based approach. Improvements are made to both types of algorithms, leading to different trade offs between arithmetic and structural complexity. We also present a new Hartley Transform algorithm with lower arithmetic complexity than any previously published one.
Room helps: Acoustic localization with finite elements

Acoustic source localization often relies on the free-space/far-field model. Recent work exploiting spatio-temporal sparsity promises to go beyond these scenarios. However, it requires the knowledge of the transfer functions from each possible source location to each microphone. We propose a method for indoor acoustic source localization in which the physical modeling is implicit. By approximating the wave equation with the finite element method (FEM), we naturally get a sparse recovery formulation of the source localization. We demonstrate how exploiting the bandwidth leads to improved performance and surprising results, such as localization of multiple sources with one microphone, or hearing around corners. Numerical simulation results show the feasibility of such schemes.
Randomized recovery for boolean compressed sensing

We consider the problem of boolean compressed sensing, which is also known as group testing. The goal is to recover a small number of defective items in a large set from a few collective binary tests. This problem can be formulated as a binary linear program, which is NP hard in general. To overcome the computational burden, it was recently proposed to relax the binary constraint on the variables, and apply a rounding to the solution of the relaxed linear program. In this paper, we introduce a randomized algorithm to replace the rounding procedure. We show that the proposed algorithm considerably improves the success rate with only a slight increase in computational cost.
Optimal MSE signal reconstruction in oversampled A/D conversion using convexity

Signal reconstruction in oversampled analog-to-digital conversion is classically performed by a lowpass filtering of the quantized signal. This leads to a mean squared error (MSE) inversely proportional to R/sup 2n+1/, where R is the oversampling rate and n is the order of the converter. It is shown that this reconstruction does not necessarily lead to a signal which gives the same digital sequence as that of the original input signal. It is shown that, if this were the case, one would obtain an MSE inversely proportional to R/sup 2n+2/ instead. A way to achieve such an estimate with the same bandwidth and same digital conversion is proposed. This made it possible to perform numerical experiments which confirm the R/sup 2n+2/ behavior of the MSE.<<ETX>>
Optimal Wavelet Packet Modulation under finite complexity constraint

Wavelet Packet Modulation (WPM) uses an arbitrary time-frequency plane tiling to create orthogonal subchannels of different bandwidths and symbol rates in a multichannel system. The Wavelet Packet Tree is implemented by iterating a perfect reconstruction two channel transmultiplexer. We derive operating conditions for the capacity-optimal tree for a given communication channel and power budget. We present a fast tree-selection algorithm which achieves this optimum for the case of a finite complexity transceiver. It is found that optimal-WPM outperforms conventional multichannel systems of equal complexity for ISI channels.
To code, or not to code: lossy source-channel communication revisited

What makes a source-channel communication system optimal? It is shown that in order to achieve an optimal cost-distortion tradeoff, the source and the channel have to be matched in a probabilistic sense. The match (or lack of it) involves the source distribution, the distortion measure, the channel conditional distribution, and the channel input cost function. Closed-form necessary and sufficient expressions relating the above entities are given. This generalizes both the separation-based approach as well as the two well-known examples of optimal uncoded communication. The condition of probabilistic matching is extended to certain nonergodic and multiuser scenarios. This leads to a result on optimal single-source broadcast communication.
Adaptive filtering in subbands with critical sampling: analysis, experiments, and application to acoustic echo cancellation

An exact analysis of the critically subsampled two-band modelization scheme is given, and it is demonstrated that adaptive cross-filters between the subbands are necessary for modelization with small output errors. It is shown that perfect reconstruction filter banks can yield exact modelization. These results are extended to the critically subsampled multiband schemes, and important computational savings are seen to be achieved by using good quality filter banks. The problem of adaptive identification in critically subsampled subbands is considered and an appropriate adaptation algorithm is derived. The authors give a detailed analysis of the computational complexity of all the discussed schemes, and experimentally verify the theoretical results that are obtained. The adaptive behavior of the subband schemes that were tested is discussed.