nils-herrmann/clean_scirepeval huggingface dataset:
Multilevel flash memory on-chip error correction based on trellis coded modulation

This paper presents a multilevel (ML) flash memory on-chip error correction system design based on the concept of trellis coded modulation (TCM). This is motivated by the non-trivial modulation process in ML memory storage and the effectiveness of TCM on integrating coding with modulation to provide better performance. Using code storage 2bits/cell flash memory as a test vehicle, the effectiveness of TCM-based systems, in terms of error-correcting performance, coding redundancy, silicon cost, and operation latency, has been successfully demonstrated
Predictive multistage vector quantizer design using asymptotic closed-loop optimization

This correspondence builds on the asymptotic closed-loop approach to predictive vector quantizer design (Khalil et al. 2001), and extends it to the design of predictive multistage vector quantizers for low bit rate video coding. The design approach resolves longstanding shortcomings, in particular, design stability and empty-cell problems. Simulation results show substantial gains over traditional design approaches.
A strictly improved achievable region for multiple descriptions using combinatorial message sharing

We recently proposed a new coding scheme for the L-channel multiple descriptions (MD) problem for general sources and distortion measures involving ‘Combinatorial Message Sharing’ (CMS)  leading to a new achievable rate-distortion region. Our objective in this paper is to establish that this coding scheme strictly subsumes the most popular region for this problem due to Venkataramani, Kramer and Goyal (VKG) . In particular, we show that for a binary symmetric source under Hamming distortion measure, the CMS scheme provides a strictly larger region for all L> 2. The principle of the CMS coding scheme is to include a common message in every subset of the descriptions, unlike the VKG scheme which sends a single common message in all the descriptions. In essence, we show that allowing for a common codeword in every subset of descriptions provides better freedom in coordinating the messages which can be exploited constructively to achieve points outside the VKG region.
Rate-distortion optimized motion estimation for error resilient video coding

Rate-distortion optimized motion compensation was originally proposed to improve the source coding efficiency of low bit-rate video coding. This paper investigates the potential benefits of extensions to explicitly handle transmission over lossy networks. The important impact of appropriate motion compensation on error resilience is identified and exploited to enhance performance. The expected end-to-end distortion, rather than the source coding distortion, is employed in the rate-distortion optimization criterion. The accuracy of the distortion estimate is crucial when optimizing over the motion vectors (as compared to the less sensitive inter/intra mode decision), and is ensured here by adopting the recursive optimal per-pixel estimate (ROPE) method. The main objective is to assess the gains achievable by such source-channel motion compensation, and this is given priority over complexity considerations. Significant performance gains in simulation demonstrate the potential of rate-distortion optimized motion compensation to improve the error resilience of video coding.
Generalized Source-Channel Prediction for Error Resilient Video Coding

This paper proposes an error-resilient modification of conventional (encoder-reconstruction based) prediction framework in video coding. The technique is called generalized source-channel prediction (GSCP) and generates a prediction reference for the next frame as a weighted sum of the current frame reconstruction and the prediction reference of the last frame. Compared to existing leaky prediction, GSCP achieves better coding efficiency in single layer video coding, and specifically better exploits the robustness benefits offered by intra coded macroblocks in past frames so as to reduce error propagation in the future frames. Significant performance gains were observed in simulations and support the effectiveness of GSCP
Hierarchical test generation for ASIC circuits using macro specification

Increasingly, VLSI or ULSI designs involve the interconnection of complex macros to produce large-scale systems on a chip. The complexity of the macros and the lack of accessibility to the macros in a chip make efficient test methods essential. Existing ATPG programs flatten designs; this discards the testability information at the macro level. The extent to which macro specification can include testability information which will allow efficient generation of test patterns for assemblages of these macros is discussed. A method of constructing a graph model for a gate-level circuit is illustrated. The graph model can be used for test pattern generation. Experimental results on a set of benchmark circuits are presented. They demonstrate that an improvement in ATPG performance is achieved when the graph model is used instead of the flattened gate-level circuit.<<ETX>>
Multistage vector quantizer optimization for packet networks

A multistage vector quantizer (MSVQ) based coding system is source-channel optimized for packet networks. Resilience to packet loss is enhanced by a proposed interleaving approach that ensures that a single lost packet only eliminates a subset of the vector stages. The design is optimized while taking into account compression efficiency, packet loss rate, and the interleaving technique in use. The new source-channel-optimized MSVQ is tested on memoryless speech line spectral frequency (LSF) parameter quantization as well as block-based image compression. With LSF coding, a source-channel-optimized MSVQ is shown to yield gains of up to 2.0 dB in signal-to-ratio (SNR) over traditional MSVQ and to substantially enhance the robustness of packetized speech transmission. Substantial gains were also obtained in the case of block-based image compression. Although the formulation is given in the context of packet networks, the work is directly extendible to the broader category of erasure channels.
Switched error concealment and robust coding decisions in scalable video coding

This work introduces two complementary techniques to improve the packet loss resilience of scalable video coding systems. First, a "switch per-pixel" error concealment (SPEC) scheme is proposed, which allows the decoder to exploit information from both the current base layer and previous enhancement-layer frame for the reconstruction of missing enhancement-layer blocks. Based on the packet loss history and the quantized base-layer data, the algorithm switches per pixel between the two information sources. SPEC is shown to consistently outperform standard concealment methods. The second main contribution is concerned with encoder decision optimization. Enhancement layer prediction modes are selected so as to minimize the overall decoder reconstruction distortion, which is due to quantization, packet loss and error propagation. The distortion computation uses a recursive optimal per-pixel estimate (ROPE) to accurately account for the effects of error concealment as well as spatial and temporal error propagation. Simulation results show that ROPE-based mode selection substantially outperforms conventional prediction mode selection schemes. Finally, the combination of SPEC at the decoder and ROPE-based mode selection at the encoder is shown to achieve significant additional performance gains.
A necessary and sufficient condition for transform optimality in source coding

It is well-known for transform coding of multivariate Gaussian sources, that the Karhunen Loeve transform (KLT) minimizes the mean square error distortion. However, finding the optimal transform for general non-Gaussian sources has been an open problem for decades, despite several important advances that provide some partial answers regarding KLT optimality. In this paper, we present a necessary and sufficient condition for optimality of a transform when high resolution, variable rate quantizers are employed. We present not only a complete characterization of when KLT is optimal, but also a determining condition for optimality of a general (non-KLT) transform. This necessary and sufficient condition is shown to have direct connections to the well studied source separation problem.  Finally, we combine the transform optimality condition with algorithmic tools from source separation, to derive a practical numerical method to search for the optimal transform in source coding.
On generalizing the estimation-theoretic framework to scalable video coding with quadtree structured block partitions

Scalable video coding suffers from the under-utilization of base layer information, where usually only the reconstruction in the base layer is used for enhancement layer prediction. Prior work from our lab proposed an optimal estimation-theoretic (ET) approach for quality scalable coding, wherein the estimates are obtained by utilizing all the available information from base layer quantization interval and enhancement layer distribution for transform coefficients. While this approach was proposed for fixed block size encoding, modern codecs employ variable block size quadtree structured partitioning, which results in different partitions at base layer and enhancement layer based on the rate-distortion trade-off, thus makes the base layer information not directly usable in the enhancement layer. Other new tools such as hybrid transform and the rate-distortion optimized quantizer (RDOQ) also have an impact on the information available for optimal estimation. In this paper, we generalize the ET framework for quality scalable video coding to account for the quadtree structured partitioning, hybrid transform and the RDOQ adjustment. Experimental evidence is provided for consistent coding gains over standard SHVC.
A classifier-based decoding approach for large scale distributed coding

Canonical distributed quantization schemes do not scale to large sensor networks due to the exponential decoder storage complexity that they entail. Prior efforts to tackle this issue have largely been limited to the suboptimal schemes of source grouping and decoding, thus failing to use all available information at the decoder. We propose a new decoding paradigm where all received bits are used in decoding. Essentially, to decode each source, we partition the space of received bit-tuples using a nearest neighbor quantizer at a decoding rate consistent with the allowed complexity and each partition is then mapped to a reconstruction value for that source. To avoid local minima in design, we resort to deterministic annealing to determine the nearest neighbor partition function (the partitioning prototypes) from the training set. Results on several data-sets show substantial gains over naive and other competing approaches.
On scalable coding in the presence of decoder side information

The problem of scalable coding while exploiting the decoder side information is considered. Prior work considered the two important cases concerning the degraded side information where source X and the side information variables (Y1, Y2) form a Markov chain in the order of either X - Y1 - Y2 or X - Y2 - Y1. While the encoding schemes for these settings differ considerably, they are both based on the combination of conditional codebook encoding, a standard tool in scalable coding, and random binning, conventionally used in decoder side information problems. In this paper, an encoding scheme is proposed solely on the basis of random binning, which essentially performs scalable and Wyner-Ziv coding simultaneously. Proposed scheme achieves the rate-distortion regions of prior results. A practical advantage of the unifying scheme is the fact that random binning can be realized via practical tools such as nested lattice codes and channel codes. Finally, motivated by the proposed encoding scheme, a network interpretation of scalable coding is considered. An achievable region is derived for this problem setting and the potential benefits of networked scalable coding are shown.
Planarization yield limiters for wafer-scale 3D ICs

The planarization requirements for 3D processing are compared to those for conventional 2D processing, indicating that wafer level planarity is essential for 3D as compared to the die level planarity needed for 2D ICs. A yield test structure has been designed to study the number of electrical faults that occur during damascene patterning. Initial experimental data with this test vehicle show that planarity changes with pattern density, although the functional relationship has not been established to date.
A nonconvex cost optimization approach to tracking multiple targets

The problem of tracking multiple targets in the presence of displacement noise and clutter is formulated as a nonconvex optimisation problem. The form of the suggested cost function is shown to be suitable for the graduated nonconvexity algorithm, which can be viewed as deterministic annealing. The method is first derived for the two-dimensional (spatial/temporal) case, and then generalized to the multidimensional case. The complexity grows linearly with the number of targets. Computer simulations show the performance with crossing trajectories.<<ETX>>
Deterministic Annealing-Based Optimization for Zero-Delay Source-Channel Coding in Networks

This paper studies the problem of global optimization of zero-delay source-channel codes that map between the source space and the channel space, under a given transmission power constraint and for the mean-square-error distortion. Particularly, we focus on two well-known network settings: the Wyner-Ziv setting where only a decoder has access to side information and the distributed setting where independent encoders transmit over independent channels to a central decoder. Prior work derived the necessary conditions for optimality of the encoder and decoder mappings, along with a greedy optimization algorithm that imposes these conditions iteratively, in conjunction with the heuristic noisy channel relaxation method to mitigate poor local minima. While noisy channel relaxation is arguably effective in simple settings, it fails to provide accurate global optimization in more complicated settings considered in this paper. We propose a powerful nonconvex optimization method based on the concept of deterministic annealing-which is derived from information theoretic principles and was successfully employed in several problems including vector quantization, classification, and regression. We present comparative numerical results that show strict superiority of the proposed method over greedy optimization methods as well as prior approaches in literature.
Radiation detection by coherent Josephson phenomena in agglomerated tin films

We have previously reported radiant energy detection by superconducting thin films in both a thermal (or bolometer) mode and a current mode. We now report evidence that at least some current-mode detection is due to Josephson behavior associated with agglomeration in tin films. The temperature dependence of the critical current agrees with results for Dayem bridges, and we observe microwave-induced steps in the current-voltage characteristics which have the proper dependence on frequency and power. At a constant film resistance the current-mode responsivity shows structure (dips) in its dependence on bias current which shifts with microwave frequency in agreement with the Josephson theory. The films appear to behave like an array of weak links in the "correlated" state as observed by Clark and explained by him and Tilley.
An error-resilient video coding framework with soft reset and end-to-end distortion optimization

Temporal prediction plays a crucial role in most video coding applications. However, due to error propagation via the prediction loop, it also increases the vulnerability to channel loss. The standard counter measure to mitigate error propagation is the ‘intra refresh’ mode, which in effect resets temporal prediction to block error propagation, but at a significant rate overhead. This on/off switch for temporal prediction is overly crude to optimize the compression-resilience tradeoff. In this paper, we propose a novel framework that significantly expands the options available to counter error propagation by introducing optimally controlled soft resets, wherein intra and inter predictions are combined with adjustable weights to control the dependency on previous frames while accounting for the overall rate and distortion. Since the optimal control of such soft resets can only be achieved if the encoder can effectively estimate its impact on the end-to-end distortion (EED), we propose to extend the well known recursive optimal per-pixel estimation (ROPE) approach to accurately account for the soft reset mode, then optimize encoder mode decisions to minimize the estimated EED for the given rate. Experimental results show that the proposed framework achieves significant performance gains for video streaming over unreliable networks.
Vector quantization with transmission energy allocation for time-varying channels

This work is concerned with the problem of designing robust, vector quantizer (VQ)-based communication systems for operation over time-varying Gaussian channels. Transmission energy allocation to VQ codeword bits, according to their error sensitivities, is a powerful tool for improving robustness to channel noise. The power of this technique can be further enhanced by appropriately combining it with index assignment methods. We pose the corresponding joint optimization problem and suggest a simple iterative algorithm for finding a locally optimal solution. The susceptibility of the solution to poor local minima is significantly reduced by an enhanced version of the algorithm which invokes the method of noisy channel relaxation whereby the VQ system is optimized while gradually decreasing the assumed level of channel noise. In a series of experiments, the resulting combined technique is shown to outperform standard pseudo-Gray coding by up to 3.5 dB and to exhibit graceful degradation at mismatched channel conditions. Finally, we extend these ideas to the case where both the transmitter and the receiver have information on the current state of a time-varying channel. The proposed method is based on switched encoding and adaptive decoding. Experimental results show that the proposed system achieves close to optimal performance.
Rate-distortion optimization and adaptation of intra prediction filter parameters

Conventional “pixel copying” prediction used in current video standards was shown in previous work to be sub-optimal compared to 2-D non-separable Markov model based recursive extrapolation approaches. The premise of this paper is that in order to achieve the full potential of these approaches it is necessary to account for several requirements, namely, the design of prediction modes (and respective extrapolation filters) must optimize a rate-distortion cost rather than minimize the mean squared prediction error; the filters must be of sufficient complexity to cover all necessary directions; and the approach must include adaptation to available information indicative of local statistics. Hence, the proposed system employs four-tap recursive extrapolation filters that can predict from all standard directions, combined with a filter design method that accounts for the overall rate-distortion cost in conjunction with the codec decisions, along with adaptation of filter coefficients to relevant local information provided by encoder decisions on target bit rate and block size. Experimental evidence is provided for substantial coding gains over conventional intra coding.
Design of robust HMM speech recognizers using deterministic annealing

We attack the difficult problem of optimizing a hidden Markov model (HMM) based speech recognizer to minimize its misclassification rate. In conventional HMM recognizer design, the training data is divided into subsets of identically labeled tokens and the HMM for each label is designed from the corresponding subset using a maximum likelihood (ML) objective. However, ML is a mismatched objective and ML design does not minimize the recognizer's misclassification rate. The misclassification rate is difficult to optimize directly because the cost surface is riddled with shallow local minima that tend to trap naive descent methods. We propose an approach which is based on the powerful technique of deterministic annealing (DA) to minimize the misclassification cost while avoiding shallow local minima. In the DA approach, the classifier's decision is randomized during design and its expected misclassification rate is minimized while enforcing a level of randomness measured by the Shannon entropy. The entropy constraint is gradually withdrawn (annealing) and in the limit, the cost function converges to the misclassification rate of a regular non-random recognizer. This algorithm is implementable by a low complexity forward-backward procedure similar to the Baum-Welch re-estimation used in ML design. Our experiments on speaker-independent isolated word speech recognition of clean and noise-corrupted utterances of letters of the difficult E-set=(b,c,d,e,g,p,t,v,z) demonstrate that DA-designed recognizers offer consistent and substantial improvements in accuracy over ML-designed recognizers.
Scalable distributed source coding

This paper considers the problem of scalable distributed coding of correlated sources that are communicated to a central unit. The general setting is typically encountered in sensor networks. The conditions of communication channels between the source encoders and fusion center may be time-varying and it is often desirable to guarantee a base layer of coarse information during channel fades. In addition the desired system should be robust to various scenarios of channel failure and should utilize all the available information to attain the best possible compression efficiency. Although a standard ‘Lloyd-style’ distributed coder design algorithm can be generalized to scalable distributed source coding, the resulting algorithm depends heavily on initialization and will virtually always converge to a poor local minimum on the distortion-cost surface. We propose an efficient initialization scheme for such a system, which employs a properly designed multi-stage distributed coder. In our prior work, we highlighted the fundamental conflict that arises when multi-stage coding is directly combined with distributed quantization. Here we use the multi-stage distributed coding system to initialize a scalable distributed coder and propose an iterative algorithm for joint design of all system components once the structural constraint is removed. Simulation results show considerable gains over randomly initialized scalable distributed coder design.