nils-herrmann/clean_scirepeval huggingface dataset:
Transmission energy allocation for CDMA applications

Transmission energy allocation (TEA) refers to redistribution of the available transmission energy among the different bits according to their sensitivity. We describe the application of TEA to robust vector quantization in a CDMA environment. We consider an interference-limited environment with negligible interference from other cells. In spite of the apparent incompatibility of TEA with such an environment, we show that it is, in fact, highly beneficial. We first analyze the effect of TEA on multi-user interference. We show that optimal TEA design is not separable from the problems of ordering of the bits of the various users, and the design of power control. We then prove that the jointly optimal solution to the overall design problem is simple and easy to implement. Finally, we provide experimental evidence to substantiate the performance gains of TEA over the standard index assignment, under conditions of severe fading.
Optimal delayed decoding of predictively encoded sources

Predictive coding eliminates redundancy due to correlations between the current and past signal samples, so that only the innovation, or prediction residual, needs to be encoded. However, the decoder may, in principle, also exploit correlations with future samples. Prior decoder enhancement work mainly applied a non-causal filter to smooth the regular decoder reconstruction. In this work we broaden the scope to pose the problem: Given an allowed decoding delay, what is the optimal decoding algorithm for predictively encoded sources? To exploit all information available to the decoder, the proposed algorithm recursively estimates conditional probability densities, given both past and available future information, and computes the optimal reconstruction via conditional expectation. We further derive a near-optimal low complexity approximation to the optimal decoder, which employs a time-invariant lookup table or codebook approach. Simulations indicate that the latter method closely approximates the optimal delayed decoder, and that both considerably outperform the competition.
Mixture of experts regression modeling by deterministic annealing

We propose a new learning algorithm for regression modeling. The method is especially suitable for optimizing neural network structures that are amenable to a statistical description as mixture models. These include mixture of experts, hierarchical mixture of experts (HME), and normalized radial basis functions (NRBF). Unlike recent maximum likelihood (ML) approaches, we directly minimize the (squared) regression error. We use the probabilistic framework as means to define an optimization method that avoids many shallow local minima on the complex cost surface. Our method is based on deterministic annealing (DA), where the entropy of the system is gradually reduced, with the expected regression cost (energy) minimized at each entropy level. The corresponding Lagrangian is the system's "free-energy", and this annealing process is controlled by variation of the Lagrange multiplier, which acts as a "temperature" parameter. The new method consistently and substantially outperformed the competing methods for training NRBF and HME regression functions over a variety of benchmark regression examples.
Silicon micromachined heat exchanger for MCMs with high temperature superconductors

The goal of this work was to develop a heat exchanger for a nine chip multi-chip module (MCM) with high temperature superconductor (HTS) interconnects, which was capable of removing 50 W/cm/sup 2/ and maintain a temperature of 80/spl deg/K. This was accomplished by forming anisotropically etched 45 /spl mu/m wide by 240 to 290 /spl mu/m deep by 5 mm long channels into a <110> orientation silicon wafer. These channels were spaced 40 /spl mu/m apart to form an efficient cooling fin structure. The silicon wafer was covered with a pyrex plate and anodically bonded to form parallel channels. Kovar fittings were attached to the pyrex plate to flow liquid nitrogen (LN/sub 2/) into the holes made in the pyrex and then through the silicon micro channel structure. This heat exchanger design is very similar to one previously developed by Tuckermann and Pease (1981), with the exceptions of the use of cryogenic temperatures, improved alignment accuracy to the {111} planes, and the HTS interconnect application. The module has been tested with polysilicon resistors as the heat source, and with a 30 W source the measured thermal resistance was well below 1/spl deg/K/W.
On zero delay source-channel coding: Functional properties and linearity conditions

In this paper, we study the zero-delay source-channel coding problem, and specifically the problem of obtaining the vector transformations that optimally map between the m-dimensional source space and the k-dimensional channel space, under a given transmission power constraint and for the mean square error distortion. We first study the functional properties of this problem and show that the objective is concave in the source and noise densities and convex in the density of the input to the channel. We then present the necessary conditions for optimality of the encoder and decoder mappings. A well known result in information theory pertains to the linearity of optimal encoding and decoding mappings in the scalar Gaussian source and channel setting, at all channel signal-to-noise ratios (CSNRs). In this paper, we study this result more generally, beyond the Gaussian source and channel, and derive the necessary and sufficient condition for linearity of optimal mappings, given a noise (or source) distribution, and a specified power constraint. We also prove that the Gaussian source-channel pair is unique in the sense that it is the only source-channel pair for which the optimal mappings are linear at more than one CSNR value. Moreover, we show the asymptotic linearity of optimal mappings for low CSNR if the channel is Gaussian regardless of the source and, at the other extreme, for high CSNR if the source is Gaussian, regardless of the channel.
Distributed predictive coding for spatio-temporally correlated sources

Distributed coding of correlated sources with memory poses a number of considerable challenges that threaten its practical application, particularly (but not only) in the context of sensor networks. This problem is strongly motivated by the obvious observation that most common sources exhibit temporal correlations that may be at least as important as spatial or intersource correlations. This paper presents an analysis of the underlying tradeoffs, paradigms for coding systems, and approaches for distributed predictive coder design optimization. Motivated by practical limitations on both complexity and delay (especially for dense sensor networks) the focus here is on predictive coding. From the source coding perspective, the most basic tradeoff (and difficulty) is due to conflicts that arise between distributed coding and prediction, wherein ldquostandardrdquo distributed quantization of the prediction errors, if coupled with imposition of zero decoder drift, would drastically compromise the predictor performance and hence the ability to exploit temporal correlations. Another challenge arises from instabilities in the design of closed-loop predictors, whose impact has been observed in the past, but is greatly exacerbated in the case of distributed coding. In the distributed predictive coder design, we highlight the fundamental tradeoffs encountered within a more general paradigm where decoder drift is allowable or unavoidable, and must be effectively accounted for and controlled. We derive an overall design optimization method for distributed predictive coding that avoids the pitfalls of naive distributed predictive quantization and produces an optimized low complexity and low delay coding system. The proposed iterative algorithms for distributed predictive coding subsume traditional single-source predictive coding and memoryless distributed coding as extreme special cases.
Efficient prediction in multiple description video coding

This work is concerned with the design of multiple description (MD) compression systems with emphasis on video coding. Although inter-frame prediction is critical to the performance of video coders, the problem of efficient prediction in MD systems has not been satisfactorily resolved. We focus on quantizer based MD coding and propose an estimation theoretic (ET) approach to prediction and reconstruction. The advantage of ET prediction is two fold: (1) It takes into account all the information available at each decoder for an optimal estimate; (2) It mitigates the degradation due to quantization in the prediction feedback loop. The ET approach is first shown to achieve significant gains in the simpler setting of predictive MD coding of synthetic Gauss-Markov and Laplace-Markov processes. We then present performance results for MD compression of video sequences and demonstrate consistent and substantial PSNR gains over conventional techniques.
Optimized Analog Mappings for Distributed Source-Channel Coding

This paper focuses on optimal analog mappings for zero-delay, distributed source-channel coding. The objective is to obtain the optimal vector trans- formations that map between m-dimensional source spaces and k-dimensional channel spaces, subject to a prescribed power constraint and assuming the mean square error distortion measure. Closed-form necessary conditions for optimality of encoding and decoding mappings are derived. An iterative de- sign algorithm is proposed, which updates encoder and decoder mappings by sequentially enforcing the complementary optimality conditions at each itera- tion. The obtained encoding functions are shown to be a continuous relative of, and in fact subsume as a special case, the Wyner-Ziv mappings encountered in digital distributed source coding systems, by mapping multiple source intervals to the same channel interval. Example mappings and performance results are presented for Gaussian sources and channels.
Modeling of power delivery into 3D chips on silicon interposer

While three-dimensional (3D) technology has several advantages for power delivery, an integrated chip-level, interposer-level, and package-level power distribution network in through-silicon-via (TSV)-based 3D system has to be modeled and evaluated. This paper reports on modeling of power delivery into 3D chip stacks on a silicon interposer/packaging substrate using a novel hybrid approach, i.e., combining the electromagnetic (EM) and analytic simulations. We intentionally partition the real stack-up structure of a 3D power network into separate components, i.e., package vias and traces, C-4 solders, interposer TSVs and planar wires, μ-C4 solders, chip TSVs, and on-chip power grids with node capacitors, decoupling capacitors and active current loads. All the passive RLGCs for each component are extracted using an EM simulation tool at a given working frequency point. We then assemble all the components back into a corresponding equivalent circuit model with those EM extracted RLGC values, thus to analyze the supply voltage (Vdd)variation over time for 3D systems in a manner of maximum accuracy and efficiency.
Interconnect constraints on BEOL manufacturing

As CMOS devices are scaled to deep submicron dimensions, BEOL manufacturability will be constrained by both global and short local interconnects. The constraints on BEOL manufacturability imposed by linewidth variability, random defects, signal integrity, and electromigration are considered for 250 and 180 nm technology. Our 250 nm projections are based on available information about Intel's 250 nm Katmai microprocessor. This design has been extended to 180 nm by doubling logic and memory. We find that signal integrity is the greatest constraint and this could be alleviated by going to copper technology.
Shared Descriptions Fusion Coding for Storage and Selective Retrieval of Correlated Sources

Motivated by sensor networks, we consider the fusion storage of correlated sources in a database, such that any subset of them may be efficiently retrieved in the future. Only statistical information about future queries is available during encoding and storage. Fusion coding of correlated sources poses new challenges due to the conflicting objectives of exploiting inter-source correlations and enabling efficient selective retrieval. Practical signal compression imposes additional constraints on system complexity. We propose a shared-descriptions approach for the design of lossy fusion coding systems, to manage the precise tradeoffs between storage rate, retrieval rate, distortion and system complexity, within one unified framework. An iterative descent algorithm is derived for the design of such fusion coders. The optimized system provides significant gains over traditional quantization techniques that are not directly optimized for fusion coding.
A generalized VQ method for combined compression and estimation

In vector quantization, one approximates an input random vector, Y, by choosing from a finite set of values known as the codebook. We consider a more general problem where one may not have direct access to Y but only to some statistically related random vector X. We observe X and would like to generate an approximation to Y from a codebook of candidate vectors. This operation, called generalized vector quantization (GVQ), is essentially that of quantized estimation. An important special case of GVQ is the problem of noisy source coding wherein a quantized approximation of a vector, Y, is obtained from observation of its noise-corrupted version, X. The optimal GVQ encoder has high complexity. We overcome the complexity barrier by optimizing a structurally-constrained encoder. This challenging optimization task is solved via a probabilistic approach, based on deterministic annealing, which overcomes problems of shallow local minima that trap simpler descent methods. We demonstrate the successful application of our method to the coding of noisy sources.
Towards predictor, quantizer and entropy coder optimality in scalable video coding

A novel coding paradigm is proposed to jointly optimize the prediction, quantization, and entropy coding modules, thereby approaching optimality in scalable video coding. It departs from conventional video coding schemes that consider prediction, transformation, quantization, and entropy coding, as largely separate sequential functional components. The method draws inspiration from an early estimation-theoretic approach, developed by our group for enhancement layer prediction, which efficiently combines all the information available to the enhancement layer coder, to produce the optimal prediction. The framework is significantly expanded here to also incorporate optimization of entropy-constrained quantization and arithmetic coding, while fully accounting for hitherto ignored relevant factors, inherent to predictive scalable coding, including information from the base layer quantization operation, and from the enhancement layer motion compensated reference. Experimental evidence is provided for substantial coding gains over conventional scalable video coding.
On zero-error source coding with decoder side information

Let (X,Y) be a pair of random variables distributed over a finite product set V/spl times/W according to a probability distribution P(x,y). The following source coding problem is considered: the encoder knows X, while the decoder knows Y and wants to learn X without error. The minimum zero-error asymptotic rate of transmission is shown to be the complementary graph entropy of an associated graph. Thus, previous results in the literature provide upper and lower bounds for this minimum rate (further, these bounds are tight for the important class of perfect graphs). The algorithmic aspects of instantaneous code design are considered next. It is shown that optimal code design is NP-hard. An optimal code design algorithm is derived. Polynomial-time suboptimal algorithms are also presented, and their average and worst case performance guarantees are established.
Near-optimal selection of encoding parameters for audio coding

We address the issue of optimizing side information rate for efficient audio coding. In coders such as the MPEG-4 AAC, at rates around 16 kbps to 48 kbps, the side information rate forms a substantial part of the total rate. The parameter search procedure in the verification model optimizes each band separately and results in poor performance at low rates. We propose to jointly optimize the encoding parameter of all the bands. The near-optimal solution using a brute force search has drastic computational complexity. However, the same solution is obtained at a much reduced complexity using a Viterbi search through a trellis. The search procedure is developed and evaluated for two objective measures, the average and the maximum noise-mask ratio. For both the measures, the trellis-based search yields substantially better solutions. In particular, trellis-based optimization of maximum noise-mask ratio greatly improves the performance of AAC at low rates. The resulting bit stream is standard-compatible, and the additional complexity due to the proposed optimization is only incurred at the encoder.
Characterization and modeling of solder balls and through-strata-vias (TSVs) in 3D architecture

3D integration is expected to lead to a semiconductor industry paradigm shift due to its tremendous benefits to performance, data bandwidth, functionality, heterogeneous integration, power and cost. In this work, we consider the case where solder balls and through-strata-vias (TSVs) are paired to electrically connect stacked chips in a vertical fashion. For the given solder-TSV configurations, transient analysis (e.g. the 3D bathtub contour) shows a good behavior of solder-TSV signal integrity in the time domain. With regard to the frequency response from 1 MHz to 10 GHz, the performances are compared among different solder-TSV configurations. Solders with diameters of 25 – 100 µm have negligible losses in their electrical performance. The gain along the signal path is reduced with the increasing number of stacked strata and eye diagrams get distorted correspondingly. As the site offset between solders and TSVs becomes large, the signal loss is exacerbated due to the loss from the needed redistribution layer (RDL). In the solder-TSV chain structure, the electrical characteristics also deteriorate with long RDLs. Two different solder-TSV arrays give very similar near-end crosstalk (NEXT) and far-end crosstalk (FEXT). In addition, the return loss, insertion loss, NEXT and FEXT of a rhombus solder-TSV array are accurately modeled by a SPICE netlist. The circuit results show very small fitting errors for the magnitude and phase of elements in the scattering matrix. This modeling approach enables 3D architecture evaluation and design using current 2D CAD tools.
Zero-error source coding with maximum distortion criterion

Let finite source and reproduction alphabets X and Y and a distortion measure d: X/spl times/Y/spl rarr/[0,/spl infin/) be given. We study the minimum asymptotic rate required to describe a source distributed over X within a (given) distortion threshold D at every sample. The problem is hence a min-max problem, and the distortion measure is extended to vectors as follows: for x/sup n//spl isin/X/sup n/, y/sup n//spl isin/Y/sup n/, d(x/sup n/, y/sup n/)=max/sub i/d(x/sub i/, y/sub i/). In the graph-theoretic formulation we introduce, a code for the problem is a dominating set of an equivalent distortion graph. We introduce a linear programming lower bound for the minimum dominating set size of an arbitrary graph, and show that this bound is also the minimum asymptotic rate required for the corresponding source. Turning then to the optimality of scalar coding, we show that scalar codes are asymptotically optimal if the underlying graph is either an interval graph or a tree.
Rate-distortion approach to databases: storage and content-based retrieval

This paper investigates the relationship between rate-distortion theory and efficient content-based data retrieval from high-dimensional databases. We consider database design as the encoding of a data object sequence, and retrieval from the database as the decoding of the sequence using side information (i.e., the query) available only at the decoder. We show that, in this setting, the optimal asymptotic tradeoff between the search time R/sub s/ (bits per data object read from the storage device) and the expected search accuracy D/sub s/ (relevance of the retrieved data set) is given by the Wyner-Ziv solution with a side-information-dependent distortion measure. Moreover, the data indexing and retrieval problem is, in general, inseparable from the data compression problem. Data items selected by the search procedure, which can be stored in the disk with a limited total rate of R/sub r/ /spl ges/ R/sub s/, need to be presented at a prescribed expected reconstruction quality D/sub r/. This is, hence, a problem of scalable source coding or successive refinement, albeit with differing layer distortion measures to quantify search and reconstruction quality, respectively. We derive a single-letter characterization of all achievable quadruples {R/sub s/,R/sub r/,D/sub s/,D/sub r/}, and prove conditions for "successive refinability" without rate loss. Finally, we show that the special case D/sub s/=D/sub r/=0 is nontrivial and of practical interest in this context, as it can impose "acceptable" search and reconstruction qualities for each individual data item and for the entire query space with high probability, in contradistinction with standard average distortion requirements. The region of achievable {R/sub s/,R/sub r/} is obtained by adapting Rimoldi's characterization to a new regular scalable coding problem.
Multimode image coding for noisy channels

We attack the problem of robust and efficient image compression for transmission over noisy channels. To achieve the dual goals of high compression efficiency and low sensitivity to channel noise we introduce a multimode coding framework. Multimode coders are quasi-fixed length in nature, and allow optimization of the tradeoff between the compression capability of variable-length coding and the robustness to channel errors of fixed length coding. We apply our framework to develop multimode image coding (MIC) schemes for noisy channels, based on the adaptive DCT. The robustness of the proposed MIC is further enhanced by the incorporation of a channel protection scheme suitable for the constraints on complexity and delay. To demonstrate the power of the technique we develop two specific image coding algorithms optimized for the binary symmetric channel. The first, MIC1, incorporates channel optimized quantizers and the second, MIC2, uses rate compatible punctured convolutional codes within the multimode framework. Simulations demonstrate that the multimode coders obtain significant performance gains of up to 6 dB over conventional fixed length coding techniques.
Jointly Optimized Mode Decisions in Redundant Video Streaming

This letter investigates source-channel coding for error-resilient video streaming using redundant encoding. We estimate the end-to-end distortion per redundantly encoded macroblock (MB) via extension of the recursive optimal per-pixel estimate to encompass redundant transmissions. Redundant encoding is formulated as joint optimization of the MB parameters in the primary and redundant transmissions. We present three encoding strategies with different gain-complexity tradeoffs. The proposed methods are general in nature, and could be implemented on top of any (hybrid) video codec. Simulation results employing H.264's redundant slice mechanism show significant performance gains over conventional error-resilient encoding methods and naive redundant encoding schemes.