nils-herrmann/clean_scirepeval huggingface dataset:
On the Information Rates of the Plenoptic Function

The plenoptic function describes the visual information available to an observer at any point in space and time. Samples of the plenoptic function (POF) are seen in video and in general visual content (images, mosaics, panoramic scenes, etc.), and represent large amounts of information. In this paper, we propose a stochastic model to study the compression limits of a simplified version of the plenoptic function. In the proposed framework, we isolate the two fundamental sources of information in the POF: the one representing the camera motion and the other representing the information complexity of the ¿reality¿ being acquired and transmitted. The sources of information are combined, generating a stochastic process that we study in detail. We first propose a model for ensembles of realities that do not change over time. The proposed model is simple in that it enables us to derive precise coding bounds in the information-theoretic sense that are sharp in a number of cases of practical interest. For this simple case of static realities and camera motion, our results indicate that coding practice is in accordance with optimal coding from an information-theoretic standpoint. The model is further extended to account for visual realities that change over time. We derive bounds on the lossless and lossy information rates for this dynamic reality model, stating conditions under which the bounds are tight. Examples with synthetic sources suggest that within our proposed model, common hybrid coding using motion/displacement estimation with DPCM performs considerably suboptimally relative to the true rate-distortion bound.
Time-frequency signal models for music analysis, transformation, and synthesis

In signal analysis-synthesis, the analysis derives a set of parameters that the synthesis uses to reconstruct the original signal. In musical applications, this reconstruction should be perceptually accurate, and the parameterization should allow for such desirable signal modifications as time-scaling, pitch-shifting and cross-synthesis; the analysis parameters should correspond to a signal model that is flexible enough to allow these transformations. Sinusoidal modeling meets this flexibility requirement, but has difficulty representing some salient features of musical signals such as attack transients and noise-like processes. Sinusoidal modeling is reviewed and some variations are proposed to account for its shortcomings; also, wavelet-based representations of musical signals are considered.
Time-varying modulated lapped transforms

We consider the problem of time-varying orthonormal tilings of the time-frequency plane. A very elegant way of obtaining such tilings is by using time-varying modulated lapped transforms. We offer ways of constructing these, by using either boundary or overlapping modulated lapped transforms The advantage of using modulated lapped transforms is that all the filters, both at transitions and within decompositions, are obtained by modulation.<<ETX>>
MOOC Adventures in Signal Processing: Bringing DSP to the era of massive open online courses

In higher education circles, 2012 may be known as the "year of the MOOC"; the launch of several high-profile initiatives, both for profit (Coursera, Udacity) and not for profit (edX), created an electrified feeling in the community, with massive open online courses (MOOCs) becoming the hottest new topic in academic conversation. The sudden attention was perhaps slightly forgetful of many notable attempts at distance learning that occurred before, from campus TV networks to well-organized online repositories of teaching material. The new mode of delivery, however, was ushered in by a few large-scale computer science courses, whose broad success triggered significant media attention .
Oversampled noisy binary image sensor

We study the oversampled binary image sensor in  under noisy scenario. The binary image sensor is similar to traditional photographic film with pixel value equal to “0” or “1”. The potential application of the oversampled binary image sensor is high dynamic range imaging. Since the pixel value is binary, we model the noise as additive Bernoulli noise. We focus on the case that the threshold in the binary sensor is equal to a single photon. Because of noise, the dynamic range of the sensor is reduced. But the image sensor is quite robust to noise when the light intensity value is large. We use maximum-likelihood estimator (MLE) to reconstruct the light intensity field, and prove that when the threshold is a single photon, even if there is noise, the log-likelihood function is still concave, which guarantees to find the global optimal solution. Experimental results for 1-D signal and 2-D images verify our performance analysis and show the effectiveness of the reconstruction algorithm.
Hartley transforms over finite fields

A general framework is presented for constructing transforms in the field of the input which have a convolution-like property. The construction is carried out over finite fields, but is shown to be valid over the real and complex fields as well. It is shown that these basefield transforms can be viewed as "projections" of the discrete Fourier transform (DFT) and that they exist for all lengths N for which the DFT is defined. The convolution property of the basefield transforms is derived and a condition for such transforms to have the self-inverse property is given. Also, fast algorithms for these basefield transforms are developed, showing gains when compared to computations using the FFT. Application of the methodology to Hartley transforms over R leads to a simple derivation of fast algorithms for computing real Hartley transforms.
A Fast Hadamard Transform for Signals With Sublinear Sparsity in the Transform Domain

In this paper, we design a new iterative low-complexity algorithm for computing the Walsh–Hadamard transform (WHT) of an $N$ dimensional signal with a $K$ -sparse WHT. We suppose that $N$ is a power of two and $K=O(N^\alpha)$ , scales sublinearly in $N$ for some $\alpha \in (0,1)$ . Assuming a random support model for the nonzero transform-domain components, our algorithm reconstructs the WHT of the signal with a sample complexity $O(K \log _{2}({N}/{K}))$ and a computational complexity $O(K\log _{2}(K) \log _{2}({N}/{K}))$ . Moreover, the algorithm succeeds with a high probability approaching 1 for large dimension $N$ . Our approach is mainly based on the subsampling (aliasing) property of the WHT, where by a carefully designed subsampling of the time-domain signal, a suitable aliasing pattern is induced in the transform domain. We treat the resulting aliasing patterns as parity-check constraints and represent them by a bipartite graph. We analyze the properties of the resulting bipartite graphs and borrow ideas from codes defined over sparse bipartite graphs to formulate the recovery of the nonzero spectral values as a peeling decoding algorithm for a specific sparse-graph code transmitted over a binary erasure channel. This enables us to use tools from coding theory (belief-propagation analysis) to characterize the asymptotic performance of our algorithm in the very sparse ( $\alpha \in (0, {1}/{3}]$ ) and the less sparse ( $\alpha \in ({1}/{3}, 1)$ ) regime. Comprehensive simulation results are provided to assess the empirical performance of the proposed algorithm.
Discrete-time wavelet extrema representation: design and consistent reconstruction

The paper studies wavelet transform extrema and zero-crossings representations within the framework of convex representations in /spl Lscr/(Z). Wavelet zero-crossings representation of two-dimensional signals is introduced as a convex multiscale edge representation as well. One appealing property of convex representations is that the reconstruction problem can be solved, at least theoretically, using the method of alternating projections onto convex sets. It turns out that in the case of the wavelet extrema and wavelet zero-crossings representations this method yields simple and practical reconstruction algorithms. Nonsubsampled filter banks that implement the wavelet transform for the two representations are also studied in the paper. Relevant classes of nonsubsampled perfect reconstruction FIR filter banks are characterized. This characterization gives a broad class of wavelets for the representations which are derived from those of the filter banks which satisfy a regularity condition.
A sampling theorem for periodic piecewise polynomial signals

We consider the problem of sampling signals which are not bandlimited, but still have a finite number of degrees of freedom per unit of time, such as, for example, piecewise polynomials. We demonstrate that by using an adequate sampling kernel and a sampling rate greater or equal to the number of degrees of freedom per unit of time, one can uniquely reconstruct such signals. This proves a sampling theorem for a wide class of signals beyond bandlimited signals. Applications of this sampling theorem can be found in signal processing, communication systems and biological systems.
Space-Frequency Quantization using Directionlets

In our previous work we proposed a construction of critically sampled perfect reconstruction transforms with directional vanishing moments (DVMs) imposed in the corresponding basis functions along different directions, called directionlets. Here, we combine the directionlets with the space-frequency quantization (SFQ) image compression method, originally based on the standard two-dimensional (2-D) wavelet transform (WT). We show that our new compression method outperforms the standard SFQ as well as the state-of-the-art compression methods, like SPIHT and JPEG-2000, in terms of the quality of compressed images, especially in a low-rate compression regime. We also show that the order of computational complexity remains the same, as compared to the complexity of the standard SFQ algorithm.
On the Information Rate of the Plenoptic Function

We study the compression problem of visual scenes acquired with a camera for transmission or storage. Our proposed model is general and includes two well-known cases: that of video coding and that of lightfield data compression. Those two examples are related in that both are characterized by two sources of complexity: the camera motion, and the scenes being acquired. The main difference of the two is in how the complexity of the camera is coded. We propose a simplified model which includes the main characteristics of the general problem. Based on this, we do theoretical analysis, develop simple codes, and show experimental results.
Shape: Linear-time camera pose estimation with quadratic error-decay

We propose a novel camera pose estimation or perspective-n-point (PnP) algorithm, based on the idea of consistency regions and half-space intersections. Our algorithm has linear time-complexity and a squared reconstruction error that decreases at least quadratically, as the number of feature point correspondences increase., Inspired by ideas from triangulation and frame quantisation theory, we define consistent reconstruction and then present SHAPE, our proposed consistent pose estimation algorithm. We compare this algorithm with state-of-the-art pose estimation techniques in terms of accuracy and error decay rate. The experimental results verify our hypothesis on the optimal worst-case quadratic decay and demonstrate its promising performance compared to other approaches.
Unlabeled sensing: Solving a linear system with unordered measurements

We study the problem of solving a linear sensing system when the observations are unlabeled. Specifically we seek a solution to a linear system of equations y = Ax when the order of the observations in the vector y is unknown. Focusing on the setting in which A is a random matrix with i.i.d. entries, we show that if the sensing matrix A admits an oversampling ratio of 2 or higher, then with probability one it is possible to recover x exactly without the knowledge of the order of the observations in y. Furthermore, if x is of dimension K, then any 2K entries of y are sufficient to recover x. This result implies the existence of deterministic unlabeled sensing matrices with an oversampling factor of 2 that admit perfect reconstruction. While the proof is constructive, it uses a combinatorial algorithm which is not practical, leaving the question of complexity open. In terms of applications, the unlabeled sensing problem is related to a popular method in robotics called simultaneous location and mapping (SLAM).
Weighted Gossip: Distributed Averaging using non-doubly stochastic matrices

This paper presents a general class of gossip-based averaging algorithms, which are inspired from Uniform Gossip . While Uniform Gossip works synchronously on complete graphs, weighted gossip algorithms allow asynchronous rounds and converge on any connected, directed or undirected graph. Unlike most previous gossip algorithms [2]–[6], Weighted Gossip admits stochastic update matrices which need not be doubly stochastic. Double-stochasticity being very restrictive in a distributed setting , this novel degree of freedom is essential and it opens the perspective of designing a large number of new gossip-based algorithms. To give an example, we present one of these algorithms, which we call One-Way Averaging. It is based on random geographic routing, just like Path Averaging , except that routes are one way instead of round trip. Hence in this example, getting rid of double stochasticity allows us to add robustness to Path Averaging.
Sampling and Reconstruction of Shapes With Algebraic Boundaries

We present a sampling theory for a class of binary images with finite rate of innovation (FRI). Every image in our model is the restriction of 1{p≤0} to the image plane, where 1 denotes the indicator function and p is some real bivariate polynomial. This particularly means that the boundaries in the image form a subset of an algebraic curve with the implicit polynomial p. We show that the image parameters-i.e., the polynomial coefficients'satisfy a set of linear annihilation equations with the coefficients being the image moments. The inherent sensitivity of the moments to noise makes the reconstruction process numerically unstable and narrows the choice of the sampling kernels to polynomial reproducing kernels. As a remedy to these problems, we replace conventional moments with more stable generalized moments that are adjusted to the given sampling kernel. The benefits are threefold: 1) it relaxes the requirements on the sampling kernels; 2) produces annihilation equations that are robust at numerical precision; and 3) extends the results to images with unbounded boundaries. We further reduce the sensitivity of the reconstruction process to noise by taking into account the sign of the polynomial at certain points, and sequentially enforcing measurement consistency. We consider various numerical experiments to demonstrate the performance of our algorithm in reconstructing binary images, including low to moderate noise levels and a range of realistic sampling kernels.
Compressive Sampling of Multiple Sparse Signals Having Common Support Using Finite Rate of Innovation Principles

In sensor networks and communication systems, one is often confronted with sampling multiple sparse signals having a common support set. Multipath channels in a multiple-input multiple-output (MIMO) wireless communication setting is an interesting example where one generally needs to perform channel estimation for each transmit-receive antenna pair. MIMO multipath channels are usually (approximately) sparse and satisfy the common-support property whenever the distances between the antennas are small compared to the distance the electromagnetic wave can travel in the time corresponding to the inverse bandwidth of the communication system. This assumption is satisfied by small and medium bandwidth communication systems like OFDM and CDMA. This leads us to extend the finite rate of innovation sampling and reconstruction scheme to the sparse common-support scenario (SCS-FRI), in which input signals contain Diracs with common locations but arbitrary weights. The goal is to efficiently reconstruct the input signals from a set of uniform samples, making use of the common-support property to improve robustness. We first find the best theoretical performance for the SCS-FRI setup by de riving the Cramér-Rao lower bound. Our results show that for a set of well-separated Diracs, it is the total energy of the Diracs at each common position which determines the bound. We then propose a multichannel reconstruction algorithm and compare its performance with the Cramér-Rao lower bound. Numerical results clearly demonstrate the effectiveness of our proposed sampling and reconstruction scheme in low SNR regimes.
Distributed compressed sensing: Sparsity models and reconstruction algorithms using annihilating filter

Consider a scenario where a distributed signal is sparse and is acquired by various sensors that see different versions. Thus, we have a set of sparse signals with both some common parts, and some variations. The question is how to acquire such signals and how to reconstruct them perfectly (noiseless case) or approximately (noisy case). We propose an extension of the annihilating filter method  to this distributed scenario. We model the inter-relation between the sparse signals by introducing three joint sparse models. For each model, we propose sensing and reconstruction algorithms that reduce the number of measurements below the limit for the single sensor scenario and results in power and bandwidth reduction in the system. In the noiseless scenario, we are close to the minimum number of measurements possible for the perfect reconstruction while by taking more measurements, we introduce redundancy in the system to effectively mitigate the noise. Simulation results justify the applicability of the approach.
Low-Complexity Video Coding for Receiver-Driven Layered Multicast

The "Internet Multicast Backbone," or MBone, has risen from a small, research curiosity to a large-scale and widely used communications infrastructure. A driving force behind this growth was the development of multipoint audio, video, and shared whiteboard conferencing applications. Because these real-time media are transmitted at a uniform rate to all of the receivers in the network, a source must either run at the bottleneck rate or overload portions of its multicast distribution tree. We overcome this limitation by moving the burden of rate adaptation from the source to the receivers with a scheme we call receiver-driven layered multicast, or RLM. In RLM, a source distributes a hierarchical signal by striping the different layers across multiple multicast groups, and receivers adjust their reception rate by simply joining and leaving multicast groups. We describe a layered video compression algorithm which, when combined with RLM, provides a comprehensive solution for scalable multicast video transmission in heterogeneous networks. In addition to a layered representation, our coder has low complexity (admitting an efficient software implementation) and high loss resilience (admitting robust operation in loosely controlled environments like the Internet). Even with these constraints, our hybrid DCT/wavelet-based coder exhibits good compression performance. It outperforms all publicly available Internet video codecs while maintaining comparable run-time performance. We have implemented our coder in a "real" application-the UCB/LBL videoconferencing tool vic. Unlike previous work on layered video compression and transmission, we have built a fully operational system that is currently being deployed on a very large scale over the MBone.
On Sampling and Coding for Distributed Acoustic Sensing

The issue of how to efficiently represent the data collected by a network of microphones recording spatio-temporal acoustic wave fields is addressed. Each sensor node in the network samples the sound field, quantizes the samples and transmits the encoded samples to some central unit, which computes an estimate of the original sound field based on the information received from all the microphones. Our analysis is based on the spectral properties of the sound field, which are induced by the physics of wave propagation and have a significant impact on the efficiency of the chosen sampling lattice and coding scheme. As field acquisition by a sensor network typically implies spatio-temporal sampling of the field, a multidimensional sampling theorem for homogeneous random fields with compactly supported spectral measures is proved. To assess the loss of information implied by source coding, rate distortion functions for various coding schemes and sampling lattices are determined. In particular, centralized coding, independent coding and some multiterminal schemes are compared. Under the assumption of spectral whiteness of the sound field, it is shown that sampling with a quincunx lattice followed by independent coding is optimal as it achieves the lower bound given by centralized coding.
Omnidirectional bats, point-to-plane distances, and the price of uniqueness

We study simultaneous localization and mapping with a device that uses reflections to measure its distance from walls. Such a device can be realized acoustically with a synchronized collocated source and receiver; it behaves like a bat with no capacity for directional hearing or vocalizing. In this paper we generalize our previous work in 2D, and show that the 3D case is not just a simple extension, but rather a fundamentally different inverse problem. While generically the 2D problem has a unique solution, in 3D uniqueness is always absent in rooms with fewer than nine walls. In addition to the complete characterization of ambiguities which arise due to this non-uniqueness, we propose a robust solution for inexact measurements similar to analogous results for Euclidean Distance Matrices. Our theoretical results have important consequences for the design of collocated range-only SLAM systems, and we support them with an array of computer experiments.
Sampling with finite rate of innovation: channel and timing estimation for UWB and GPS

In this work, we consider the problem of channel estimation by using the recently developed theory for sampling of signals with a finite rate of innovation. We show a framework which allows for lower than Nyquist rate sampling applicable for timing and channel estimation of both narrowband and wideband channels. In certain cases we demonstrate performance exceeding that of algorithms using Nyquist rate sampling while working at lower sampling rates, thus saving power and computational complexity.
Howis the weather: Automatic inference from images

Low-cost monitoring cameras/webcams provide unique visual information. To take advantage of the vast image dataset captured by a typical webcam, we consider the problem of retrieving weather information from a database of still images. The task is to automatically label all images with different weather conditions (e.g., sunny, cloudy, and overcast), using limited human assistance. To address the drawbacks in existing weather prediction algorithms, we first apply image segmentation to the raw images to avoid disturbance of the non-sky region. Then, we propose to use multiple kernel learning to gather and select an optimal subset of image features from a certain feature pool. To further increase the recognition performance, we adopt multi-pass active learning for selecting the training set. The experimental results show that our weather recognition system achieves high performance.