nils-herrmann/clean_scirepeval huggingface dataset:
On distributed sampling of bandlimited and non-bandlimited sensor fields

Distributed sampling and reconstruction of a physical field using an array of sensors is a problem of increasing interest in environmental monitoring applications of sensor networks. We show, using a dither-based scheme, that it is possible to reconstruct non-bandlimited fields with a reconstruction accuracy that depends on the available bitrate R and the spectral decay characteristics of the sensor field - we study exponentially decaying spectra as an illustration. For bandlimited fields f(t), the maximum pointwise error D/sub f/ decays as D/sub f/ /spl sim/ 2/sup -a1R/, i.e. exponentially with rate R. For the non-bandlimited case, we show that for fields u(t) with exponentially decaying spectral tails, i.e., |U(/spl omega/)|/spl sim/e/sup -a|/spl omega/|/, the maximum pointwise error D/sub u/ decays as D/sub u//spl sim/e/sup -a2/spl radic/R/(1+o(R)) with spatial bit rate R bits/metre. We also show that it is possible to trade off the number of sensors with their precision, while maintaining a similar reconstruction accuracy - a phenomenon that may be dubbed as a "bit-conservation" principle underlying the sampling framework.
n-channel multiple descriptions: theory and constructions

We present new achievable rate regions and code constructions for the symmetric n-channel multiple descriptions (MD) coding problem (Puri et al. (2002)) for n>2. Our approach is inspired by unexplored connections between MD and the problem of distributed source coding (Slepian et al. (1973); Wyner et al. (1976)). For illustrative clarity, we restrict our focus to the important special case relating to (n, k) source-channel erasure codes (Pradhan et al. (2001)). This involves n encodings of a source with the goal of maximizing its reconstruction fidelity with the availability of any k of them, while strictly improving this reconstruction fidelity with the availability of more than k descriptions. We describe the underlying information-theoretic framework, and then formulate practical constructions based on scalar quantizers and linear channel codes for the n=3 case to illustrate our concepts.
Codes for a distributed caching based Video-on-Demand system

We study the role of codes in the optimization and design of a large-scale Video-on-Demand (VoD) system based on distributed caching, that we have architected and built at Berkeley. We show how network codes can convert a combinatorial problem into a tractable one, and enable a fully distributed algorithm that jointly optimizes the three-fold problem of cache content placement, cache-to-users topology selection, and cache-to-users rate-allocation. While a description of the general VoD system optimization and design can be found in , this paper focuses on the critical role of codes in enabling our VoD system. Specifically, we motivate and describe a specific class of network codes, called DRESS codes, that offer desirable tradeoffs between cache-to-user and cache-to-cache communication aspects of the problem needed to sustain a scalable VoD system.
On the duality between distributed source coding and data hiding

Inspired by a previously proposed constructive framework for the distributed source coding problem, we propose a powerful constructive approach to the watermarking problem, emphasizing the dual roles of "source codes" and "channel codes". In our framework, we explore various source and channel codes to achieve watermarks that are robust to attackers in terms of maximizing the distortion between the corrupted coded-source signal and the original signal while holding the distortion between the coded-source signal and the original signal constant. Simulations show that performance gains are accredited to a careful choice of source and channel codes.
Optimal smart grid tariffs

In this paper, we develop a low-complexity scheme OpTar for designing optimal power tariffs. OpTar provides an easy way for the utility companies to adjust their power prices, and allows the users to pre-plan their power consumption. OpTar is robust against system dynamics including time-varying power market conditions and user preferences. It can be implemented both with and without communication between the utility company and the consumers, and requires minimum additional hardware/software. We show that OpTar can significantly outperform the commonly used uniform pricing schemes. The pricing design approach developed in the paper can also be used in other systems with dynamic demand and non-storable resources.
Efficient layered video delivery over multicarrier systems using optimized embedded modulation

We tackle the problem of efficient image transmission over multi-carrier modulation (MCM) systems, proposing the use of a layered or multiresolution (MR) framework. In this work, we treat the source as being characterized by multiple layers of importance, and therefore deserving of multiple levels of noise immunity, i.e. having different BER requirements. We present the idea of embedded multi-carrier modulation (EMCM) as a very effective way of achieving this, and introduce a fast table-lookup based power allocation algorithm that optimizes the multicarrier constellation design in terms of maximizing the deliverable throughput bitrates for the different resolution layers, subject to a total power constraint. Simulation results of our EMCM system reveal substantial gains (up to about 25%) in deliverable bit rates over optimized TDM-based MCM designs. Further, in typical image transmission simulations using an embedded wavelet image coder, the EMCM approach yields almost 3 dB gains in delivered quality over conventional single-resolution MCM systems.
Speeding up distributed machine learning using codes

Distributed machine learning algorithms that are widely run on modern large-scale computing platforms face several types of randomness, uncertainty and system “noise.” These include stragglers1, system failures, maintenance outages, and communication bottlenecks. In this work, we view distributed machine learning algorithms through a coding-theoretic lens, and show how codes can equip them with robustness against this system noise. Motivated by their importance and universality, we focus on two of the most basic building blocks of distributed learning algorithms: data shuffling and matrix multiplication. In data shuffling, we use codes to reduce communication bottlenecks: when a constant fraction of the data can be cached at each worker node, and n is the number of workers, coded shuffling reduces the communication cost by up to a factor Θ(n) over uncoded shuffling. For matrix multiplication, we use codes to alleviate the effects of stragglers, also known as the straggler problem. We show that if the number of workers is n, and the runtime of each subtask has an exponential tail, the optimal coded matrix multiplication is Θ(log n) times faster than the uncoded matrix multiplication or the optimal task replication scheme.
Optimal subband filter banks for multiple description coding

Multiple description coding refers to the encoding of an information source into multiple bit streams, in a manner that each bit stream independently represents a "coarse" description of the source, while multiple descriptions jointly convey a higher fidelity source representation. Thus multiple description coding allows enhanced resilience to loss of individual descriptions, and can be viewed as a form of source-based diversity coding. This paper addresses the design of subband filter banks to optimize the rate-distortion performance of multiple description subband coding. The problem is formulated in a Lagrangian optimization framework, and is solved directly in the frequency domain to produce the optimal filter spectral responses. By varying the Lagrangian parameter, our design obtains all points on the redundancy-distortion curve for Gaussian wide-sense stationary input processes. We consider the cases of both orthonormal and more general biorthogonal filter banks. We also analyze the connections between these two solutions, and quantify the gap in their optimal performance. Application of the proposed methods to the popular first-order autoregressive (AR(1)) model yields very interesting and insightful results. In the two extreme cases of maximum and minimum redundancy, our solutions degenerate to previously known results. Finally, comparisons with popularly deployed filter banks reveal large gaps from the theoretical performance bound.
Unsupervised Discovery of Action Hierarchies in Large Collections of Activity Videos

Given a large collection of videos containing activities, we investigate the problem of organizing it in an unsupervised fashion into a hierarchy based on the similarity of actions embedded in the videos. We use spatio-temporal volumes of filtered motion vectors to compute appearance-invariant action similarity measures efficiently -and use these similarity measures in hierarchical agglomerative clustering to organize videos into a hierarchy such that neighboring nodes contain similar actions. This naturally leads to a simple automatic scheme for selecting videos of representative actions (exemplars) from the database and for efficiently indexing the whole database. We compute a performance metric on the hierarchical structure to evaluate goodness of the estimated hierarchy, and show that this metric has potential for predicting the clustering performance of various joining criteria used in building hierarchies. Our results show that perceptually meaningful hierarchies can be constructed based on action similarities with minimal user supervision, while providing favorable clustering performance and retrieval performance.
Optimized filtering and reconstruction in predictive quantization with losses

Consider a communication system in which a filtered and quantized signal is sent over a channel with erasures and (potentially) additive noise. Linear MMSE estimation is achieved in such a system by Kalman filtering. Allowing any Markov erasure process and any Markov-state jump linear signal generation model, it is shown that the estimation performance at the receiver can be computed as a deterministic optimization with linear matrix inequality (LMl) constraints rather than a pseudorandom simulation. Furthermore, in contrast to the case without erasures, the filtering in the transmitter should not necessarily be MMSE prediction (whitening); a procedure is given to find a locally optimal prefilter. The main tools are recent LMI characterizations of asymptotic state estimation error covariance and output estimation error variance for discrete-time jump linear systems in which the discrete portion of the system state is a Markov chain. As another application of this framework, a novel analysis and optimization of a "streaming" version of multiple description coding based on subsampling is outlined.
The SPRIGHT algorithm for robust sparse Hadamard Transforms

In this paper, we consider the problem of computing a K-sparse N-point Hadamard Transforms (HT) from noisy time domain samples, where K = O(Nα) scales sub-linearly in N for some α ∈ (0; 1). The SParse Robust Iterative Graph-based Hadamard Transform (SPRIGHT) algorithm is proposed to recover the sparse HT coefficients in a stable manner that is robust to additive Gaussian noise. In particular, it is shown that the K-sparse HT of the signal can be reconstructed from noisy time domain samples with a vanishing error probability using the same sample complexity O(K logN) as in the noiseless case of  and computational complexity1 O(N logN). Last but not least, given the complexity orders of the SPRIGHT algorithm, our numerical experiments further validate that the big-Oh constants in the complexity are small.
Computing a k-sparse n-length Discrete Fourier Transform using at most 4k samples and O(k log k) complexity

Given an n-length input signal x, it is well known that its Discrete Fourier Transform (DFT), X, can be computed in O(nlogn) complexity using a Fast Fourier Transform. If the spectrum X is exactly k-sparse (where k <;<; n), can we do better? We show that asymptotically in k and n, when k is sub-linear in n (i.e., k ∝ nδ where 0 <; δ <; 1), and the support of the non-zero DFT coefficients is uniformly random, we can exploit this sparsity in two fundamental ways (i) sample complexity: we need only M = rk deterministically chosen samples of the input signal x (where r <; 4 when 0 <; δ <; 0.99); and (ii) computational complexity: we can reliably compute the DFT X using O(k log k) operations, where the constants in the big Oh are small. Our algorithm succeeds with high probability, with the probability of failure vanishing to zero asymptotically in the number of samples acquired, M. Our approach is based on filterless subsampling of the input signal x using a small set of carefully chosen uniform subsampling patterns guided by the Chinese Remainder Theorem (CRT). Specifically, our subsampling operation on x is designed to create aliasing patterns on the spectrum X that "look like" parity-check constraints of good erasure-correcting sparse-graph codes. We show how computing the sparse DFT X is equivalent to decoding of these sparse-graph codes and is low in both sample complexity and decoding complexity. We accordingly dub our algorithm the FFAST (Fast Fourier Aliasing-based Sparse Transform) algorithm. In our analysis, we rigorously connect our CRT based graph constructions to random sparse-graph codes based on a balls-and-bins model and analyze the convergence behavior of the latter using well-studied density evolution techniques from coding theory. We provide simulation results in Section IV that corroborate our theoretical findings, and validate the empirical performance of the FFAST algorithm.
Compressing encrypted sources using side-information coding

When transmitting a source over an insecure and bandwidth-limited channel, compression (lossy/lossless) precedes encryption. We show that, through the use of side-information coding principles, the order of these operations can be reversed without loss of Wyner-sense perfect secrecy and often with a significant compression ratio. Further, when the source has to be recovered perfectly (with high probability) or is Gaussian (with the mean-squared error fidelity criterion), there is no loss of compression efficiency and the proposed system requires no more randomness in the encryption key compared to systems where compression precedes encryption.
List Viterbi decoding with continuous error detection for magnetic recording

The list Viterbi algorithm (LVA) with an arithmetic coding based continuous error detection (CED) scheme is applied to high-order partial-response magnetic recording channels. Commonly used magnetic recording systems employ distance-enhancing codes along with parity-check post-processors to correct most dominant error events. The system presented here inserts a CED encoder between the outer Reed-Solomon (RS) code and the channel, and utilizes LVA along with CED's error detection capabilities to improve the performance of the Viterbi decoder. Simulations show that this system results in a 2 dB improvement over MTR encoded EEPRML decoding at a BER of 2/spl times/10/sup -6/ in additive white Gaussian noise and localizes error occurrences to the end of the sector.
Error-Tolerant SRAM Design for Ultra-Low Power Standby Operation

We present an error-tolerant SRAM design optimized for ultra-low standby power. Using SRAM cell optimization techniques, the maximum data retention voltage (DRV) of a 90 nm 26 kb SRAM module is reduced from 550 mV to 220 mV. A novel error-tolerant architecture further reduces the minimum static-error-free VDD to 155 mV. With a 100 mV noise margin, a 255 mV standby VDD effectively reduces the SRAM leakage power by 98% compared to the typical standby at 1 V VDD.
Distributed compression in a dense microsensor network

Distributed nature of the sensor network architecture introduces unique challenges and opportunities for collaborative networked signal processing techniques that can potentially lead to significant performance gains. Many evolving low-power sensor network scenarios need to have high spatial density to enable reliable operation in the face of component node failures as well as to facilitate high spatial localization of events of interest. This induces a high level of network data redundancy, where spatially proximal sensor readings are highly correlated. We propose a new way of removing this redundancy in a completely distributed manner, i.e., without the sensors needing to talk, to one another. Our constructive framework for this problem is dubbed DISCUS (distributed source coding using syndromes) and is inspired by fundamental concepts from information theory. We review the main ideas, provide illustrations, and give the intuition behind the theory that enables this framework.We present a new domain of collaborative information communication and processing through the framework on distributed source coding. This framework enables highly effective and efficient compression across a sensor network without the need to establish inter-node communication, using well-studied and fast error-correcting coding algorithms.
A wavelet zerotree-based hybrid compressed/uncompressed framework for wireless image transmission

We consider the problem of efficient image transmission over noisy time-varying channels subject to a low transmission energy constraint (and fixed bandwidth/delay constraints). We examine the limits of desirability of a highly compressed representation using a joint source-channel coding (JSCC) framework. Specifically, invoking as a platform a state-of-the-art wavelet image coder, we demonstrate how the resulting highly compressed digital stream, appropriately protected against channel noise, is not always the best solution. We show how a hybrid scheme based on simple partitioning of the wavelet image representation into "compressed" and "uncompressed" components can lead to significantly improved performance (of the order of 3 dB in PSNR for Rayleigh channels) over popular JSCC schemes which are based on compressed, entropy-coded, and appropriately unequal error-protected (UEP) source representations.
Correction to "on functional duality in multiuser source and channel coding problems having one-sidedcollaboration"

In the above titled paper (ibid., vol. 52, no. 7, pp. 2986-3002, Jul. 06), there is an error on line 8 in the first paragraph on the left column on page 2992 regarding broadcast channels. The equation is not correct and thus the argument given in the rest of the paragraph is not valid. A correct argument is presented here.
One-Way Private Media Search on Public Databases: The Role of Signal Processing

Automated media classification is becoming increasingly common in areas ranging from mobile location recognition to surveillance systems to automated annotation. While these tools can add great value to the public sphere, media searches often process private information; in such situations, it is important to protect the interests of one or both parties. Much attention has been given to the scenario where both the server and the client wish to keep their data secret, but comparatively little work has been done on searches in which only the clients data is sensitive. Nonetheless, there is great potential for applications involving private searches on public databases like Google Images, Flickr, or Wanted Persons directories put forth by various police agencies. In this article, we make the case that one-way private media search is an important and practically viable direction for future research. We will introduce readers to some basic one-way privacy tools and present a case study outlining the design of a private audio search tool on a public database. This case study serves as a backdrop for a discussion on the role of signal processing techniques in the design of privacy-preserving media search systems.
On secure distributed data storage under repair dynamics

We address the problem of securing distributed storage systems against passive eavesdroppers that can observe a limited number of storage nodes. An important aspect of these systems is node failures over time, which demand a repair mechanism aimed at maintaining a targeted high level of system reliability. If an eavesdropper observes a node that is added to the system to replace a failed node, it will have access to all the data downloaded during repair, which can potentially compromise the entire information in the system.We are interested in determining the secrecy capacity of distributed storage systems under repair dynamics, i.e., the maximum amount of data that can be securely stored and made available to a legitimate user without revealing any information to any eavesdropper. We derive a general upper bound on the secrecy capacity and show that this bound is tight for the bandwidth-limited regime which is of importance in scenarios such as peer-to-peer distributed storage systems. We also provide a simple explicit code construction that achieves the capacity for this regime.
A piggybacking design framework for read-and download-efficient distributed storage codes

We present a new piggybacking framework for designing distributed storage codes that are efficient in the amount of data read and downloaded during node-repair. We illustrate the power of this framework by constructing explicit codes that attain the smallest amount of data to be read and downloaded for repair among all existing solutions for three important settings: (a) codes meeting the constraints of being maximum distance separable (MDS), high-rate, and having a small number of substripes, (b) binary MDS codes for all parameters where binary MDS codes exist, and (c) MDS codes with the smallest repair-locality. In addition, we show how to use this framework to enable efficient repair of parity nodes in existing codes that are constructed to address the repair of only the systematic nodes. The basic idea behind this framework is to take multiple stripes of existing codes and add carefully designed functions of the data of one stripe to other stripes. Typical savings in the amount of data read and downloaded during repair are 25% to 50% depending on the choice of the system parameters.
Multi-camera video resolution enhancement by fusion of spatial disparity and temporal motion fields

We consider the problem of spatio-temporal resolution enhancement of a dynamic video scene by synergistically combining information from multiple video sequences corresponding to the different views of the scene. While prior work has focused on spatio-temporal superresolution by primarily exploiting the blurring correlation induced by the scene imaging process, in this work we exploit another and perhaps a stronger form of correlation the intrinsic correlation present in the dynamic scene. We use the knowledge base from the areas of multi-view computer vision, video coding and signal/image processing to efficiently model the dynamic video scene by exploiting inter-camera (space) as well as intra-camera correlations (time). We present simulation results corresponding to a lowframe rate two-camera stereo video configuration where the sequences are combined to generate higher frame-rate sequences. Our proposed framework has direct application to problems such as multi-view video surveillance and relates closely to problems such as multi-view video compression.
Joint source-channel matching for a wireless communications link

Application of joint source-channel matching in heterogeneous multimedia environments will demand general source-channel optimization schemes suitable for a wide variety of source coding standards, channel coders, and variable channel conditions. We develop a general approach for joint source-channel matching based on a parametric distortion model that can be accurately applied to most classes of source and channel coders. Our simulations indicate that it may be possible to obtain nearly all of the benefits of joint source-channel optimization by matching existing source and channel coding standards using the simple and general approach we propose.