nils-herrmann/clean_scirepeval huggingface dataset:
Multiuser diversity with quantized feedback

In this paper, we propose an optimal discrete rate switch-based multiuser diversity (DSMUDiv) scheduling scheme that reduces the feedback load while preserving most of the performance of opportunistic scheduling. In order to reduce the feedback rate, quantized values indicating the modulation level are feedback instead of the full values of the SNRs (quantized SNRs). We examine the DSMUDiv scheme using two scheduling criteria depending on the distribution of the mobile users in the cell: (i) absolute signal-to-noise ratio (SNR)-based scheduling in the case of independent and identical distributed (i.i.d.) users, and (ii) normalized SNR-based scheduling in the case of i.-non-i.d. users. The paper includes the derivation of closed-form expressions of the feedback load, spectral efficiency and probability of access. Monte Carlo simulation is used to evaluate the spectral efficiency in the case of normalized SNR-based scheduling. We compare our scheduling scheme under slow Rayleigh fading assumption with the optimal (full feedback load) selective diversity scheduling scheme
Sequential techniques in hierarchical radar target localization

In this paper we investigate the use of multi-hypothesis sequential detection techniques to enhance effort allocation in hierarchical radar target localization in the presence of white Gaussian noise. This is done in the context of a discretized version of the problem of optimal beam-forming, or radar transmit and receive pattern design. We assume that the target is equally likely to be in one of M discrete cells and that we have L observations at our disposal. We recursively group the search cells into m groups until the size of each group reduces to one cell, thus creating an m-ary search tree of depth log/sub m/(M). We then allocate the available L observations among the tree levels in a manner that maximizes the probability of correctly locating the target. The main contribution of this paper is the use of multi-hypothesis sequential detection along with on-line effort allocation to enhance the performance of a constrained off-line allocation strategy. This approach is shown to have superior performance compared with the previously proposed unconstrained off-line allocation strategy.
Robust high capacity data embedding

We present a novel algorithm for high capacity data embedding. The proposed algorithm gives high hiding ratios up to 1/13 (1 embedded bit out of 13 raw image pixels) subject to JPEG compression with quality factor equals 75. With such high capacity, one can easily embed some important regions of an image inside the image itself with very small perceptual distortion. In the proposed algorithm, the DCT coefficients of 8/spl times/8 blocks are first rearranged using Hilbert curves. Data is embedded using projections on a random orthogonal set. The algorithm recovers the embedded data without any reference to the original image, with very low BER (around 0.1%). Finally, the proposed algorithm shows robustness to JPEG compression.
Single and multi-carrier UWB communications

In this paper we study single and multi-carrier ultra-wideband (UWB) communications focusing on spectral efficiency and system performance. We begin by examining the spectrum of the general form of single carrier ultra-wideband systems and argue that multi-carrier UWB systems provide better utilization of the available spectrum. We then describe a general form for multi-carrier UWB systems and study their performance focusing on the multipath interference (MI) and interchannel interference (ICI). The results of this analysis lead us to a signal design approach for a multi-earner UWB system named UWB-OFDM.
Data embedding in audio using time-scale modification

A new framework for data embedding in audio is proposed. The basic idea of the algorithm is to change the length of the intervals between salient points of the audio signal to embed data. The intervals are quantized and the data is embedded in the quantization indices. In our particular implementation, we use the wavelet extrema of the signal envelope as the salient points. We propose novel ideas for practical implementation that can be used by other data embedding schemes as well. The algorithm is robust to common audio processing operations, e.g., mp3 lossy compression, low pass filtering, sampling rate conversion, and time-scale modification (TSM). The perceptual quality of the audio signal after embedding the data depends on the technique used for TSM.
Pilot aided direction of arrival estimation for mmWave cellular systems

Direction of arrival (DoA) estimation of high-resolution beams is critical for cell search and maintaining communication in millimeter wave (mmWave) cellular systems. All-digital solutions for DoA estimation, though desirable for their flexibility and performance, are impractical because of their use of high-speed, power hungry Analog-Digital Converters (ADCs) at each antenna element. In this paper we take a novel approach to formulate a fully digital DoA estimation solution. Our method utilizes mmWave propagation characteristics to properly design a pilot signal that enables the reduction of the ADC speed and the number of antenna by a significant amount while achieving a desired performance goal. Our proposed method applies subspace-based methods like MUSIC on the heavily subsampled received signals, and performs the DoA estimation on a small subset of the dominant multi-paths in the channel one at a time, thus maintaining the integrity of the DoA estimation algorithm while significantly reducing computational complexity.
Direction finding in the presence of colored noise by candidate identification

A method is developed for identifying correct angles of arrivals from a set of candidate angles that contains spurious angles and the true ones, under the assumption that the colored noise is an autoregressive process and that a uniform linear array of sensors is used. The procedure is based on a relation derived for the higher-order reflection coefficients corresponding to the autocorrelation sequence of the signal plus noise. It is shown that the higher-order reflection coefficients of a set of plane waves impinging on a uniform linear array of sensors, in the presence of an unknown spatially autoregressive colored noise process, are equal to an order independent linear combination of the values of the Fourier transform of the corresponding optimal linear least squares normalized prediction error filter at the electric phase angles of the plane waves. The magnitudes of the coefficients are then used to decide whether a given candidate direction of arrival corresponds to an actual plane wave or not. The estimation of candidate angles is also briefly discussed.
A sparse solution to the bounded subset selection problem: a network flow model approach

We reformulate the problem of finding the sparsest representation of a given signal using an overcomplete dictionary as a bounded error subset selection problem. Specifically, the reconstructed signal is allowed to differ from the original signal by a bounded error. We argue that this bounded error formulation is natural in many applications, such as coding. Our novel formulation guarantees the sparsest solution to the bounded error subset selection problem by minimizing the number of nonzero coefficients in the solution vector. We show that this solution can be computed by finding the minimum cost flow path of an equivalent network. Integer programming is adopted to find the solution.
Compressed sensing and multistatic SAR

We demonstrate that the remarkable advantages of compressed sensing remain in force when the information operator is constrained to obey the physical rules of a multistatic SAR measurement. The design guidelines of the SAR information operator for ℓ2 reconstructions is compared to those provided for generic ℓ1 reconstructions. We report little or no degradation in compression performance when using an information operator obeying SAR sampling constraints. Simulations for a Shepp-Logan image show an image is faithfully reconstructed when the number of measurements is about a third of the number of image pixels, using a minimum total-variation technique. We observed high sensitivity in performance and algorithm convergence to small perturbations in the measurement vectors.
Detection of insect damaged wheat kernels by impact acoustics

Insect damaged wheat kernels (IDK) are characterized by a small hole bored into the kernel by insect larvae. This damage decreases flour quality as insect proteins interfere with the bread-making biochemistry and insect fragments are very unsightly. A prototype system was set up to detect IDK by dropping them onto a steel plate and processing the acoustic signal generated when kernels impact the plate. The acoustic signal was processed by three different methods: (1) modeling of the signal in the time domain; (2) computing time domain signal variances in short time windows; and (3), analysis of the frequency spectra magnitudes. Linear discriminant analysis was used to select a subset of features and perform classification. 98% of un-damaged kernels and 84.4% of IDK were correctly classified.
Movement direction decoding of local field potentials using time-evolving spatial patterns

A main disadvantage of using intra-cortical recordings for Brain Computer Interface (BCI) is their inherent non-stationarity and instability. Thus developing direction decoders for Local Field Potentials (LFP) that are robust over time becomes a difficult task. In this paper, we show the superior performance of qualitative information over the absolute power of the recorded signals by introducing a novel method, that uses time-evolving spatial patterns. This method over-performs the baseline method by 30% on an average over a two week testing period and provides a bit-rate of 0.98 per trial. Further, these spatial-patterns provide robustness against learning when new field-forces are introduced.
Subband domain coding of binary textual images for document archiving

In this work, a subband domain textual image compression method is developed. The document image is first decomposed into subimages using binary subband decompositions. Next, the character locations in the subbands and the symbol library consisting of the character images are encoded. The method is suitable for keyword search in the compressed data. It is observed that very high compression ratios are obtained with this method. Simulation studies are presented.
A nonuniform quantization scheme for high speed SAR ADC architecture

We introduce a new signal sampling scheme which allows high quality signal conversion to overcome the constraint of effective number of bits in high speed signal acquisition. The proposed scheme is based on the popular successive approximation register (SAR) and employs compressive sensing technique to increase the resolution of a SAR analog-to-digital converter (ADC) architecture. We present signal acquisition and recovery model which provides better performance in signal acquisition. The sampled signal shows higher resolution after recovery than conventional compressive sensing based sampling schemes. Circuit level architecture is discussed to implement the proposed scheme using the SAR ADC architecture. Simulation result shows that the proposed nonuniform quantization strategy can be a way to overcome the sampling rate-resolution limitation which is a challenging problem in SAR ADC design even with the most advanced technology.
Acoustic emission transient detection based on linear model residuals

Acoustic emissions (AE), which are ultrasonic waves created by the formation/propagation of a crack in a material, provide a possible avenue for automatic fault monitoring in machinery. In metal components, these AE signals are very complex transients that exhibit rapid time-varying behaviour due to multiple modes and multipath propagation. We present a scheme for the detection of AE transients that requires minimal a priori assumptions on the signal model. The technique, that is amenable to both adaptive and block implementations, is based on the thresholding of the ratio of the residual energy in fitting the transient to a linear model, to the signal energy. The method works well in detecting transients even at low SNR.
Signal Processing Techniques and Statistics for the Analysis of Human Genome Associated with Behavior Abnormalities

Almost all human genetic diseases such as cancers and developmental abnormalities are characterized by the presence of genetic variations. Microrray-based Comparative Genomic Hybridization techniques are used to map and measure DNA copy number variations with high-resolution. However, the observed copy numbers are corrupted by noise, making variations breakpoints hard to detect. In this paper, we provide a framework for the analysis of copy number datasets and it is divided into two parts. In the first part, we propose a novel image processing technique to analyze copy number variations based on extended version of Sigma filter algorithm as pre-processing technique. In the second part, we provide statistical searching model for classifying nonrandom genomic variations across multiple samples. Finally, we provide simulated and real data samples to study this effect.
ULV and generalized ULV subspace tracking adaptive algorithms

Traditional adaptive filters assume that the effective rank of the input signal is the same as the input covariance matrix or the filter length N. Therefore, if the input signal lives in a subspace of dimension less than N, these filters fail to perform satisfactorily. In this paper, we present two new algorithms for adapting only in the dominant signal subspace. The first of these is a low-rank recursive-least-squares (RLS) algorithm that uses a ULV decomposition (Stewart 1992) to track and adapt in the signal subspace. The second adaptive algorithm is a subspace tracking least-mean-squares (LMS) algorithm that uses a generalized ULV (GULV) decomposition, developed in this paper, to track and adapt in subspaces corresponding to several well-conditioned singular value clusters. The algorithm also has an improved convergence speed compared with that of the LMS algorithm. Bounds on the quality of subspaces isolated using the GULV decomposition are derived, and the performance of the adaptive algorithms are analyzed.
Guest editorial special issue on signal processing for data hiding in digital media and secure content delivery

T HE “Digital Revolution,” along with the recent developments in the Internet, have made the access of information convenient to users and has increased its consumption tremendously. However, this technological progress has also made digital data very vulnerable to unauthorized use and has caused significant economical losses for the content producers and rights holders. Therefore, there has been a flurry of research and development effort in the field of information security technology and systems for various applications spanning multimedia to military. Data hiding is one of the promising technologies helping to achieve the overall goal of secure delivery of information from its source to the authorized end-users. Although there are several early applications of data hiding (e.g., audio and video watermarking, broadcast monitoring, secret communications), we expect to see many other uses of data hiding technology in the future. The theoretical frameworks of data hiding are maturing, but its potential merit very much depends on the nature of a given application scenario. Therefore, subjective measures play a crucial role in the design of data hiding algorithms and their fine tuning. There are many ways to hide data in a cover. This fact makes it a very challenging task, theoretically and practically, to detect any hidden data in an arbitrary cover signal, given that the data hiding method is unknown at the detector. This special issue comprises contributions from researchers in data hiding. We would like to thank them, and the reviewers,
Scalable cryptographic scheme for networked multimedia applications

Computer security has become the main stream issue to protect the sensitive information in open networks. The complete secure system is hard to design these days due to vulnerabilities in firewalls, file and application servers, email servers, and Web servers. The solution to problems of computer security and privacy can be achieved by the cryptography mechanism. A popular way to implement security protocols uses a combination of several cryptographic schemes to overcome various limitations of security primitives. The design of scalable security protocols that are interrelated with a single cryptosystem for multimedia is a challenging cryptographic technology in the cryptologic research community. We propose the scalable cryptographic scheme for security services. The proposed eigen-based security primitives solves a wide range of secrecy, non-repudiation, authenticity, and integrity problems in many security aware multimedia applications. The new cryptography technology may play an important role in the trusted network interpretation environments.
CODING FOR CONTENT-BASED RETRIEVAL

We define a data structure called a "web" together with an algorithm to choose scale-space atoms for representing an image. The corresponding wavelet coefficients (of the atoms chosen using this method) have useful properties which lead to (i) the definition of a stochastic process for representing images and (ii) an efficient image compression algorithm. The advantage of our image compression algorithm is that the computational requirement is very low. The stochastic process is useful in a theoretical sense because it gives us a framework in which to understand images and certain image compression algorithms.
Parametrized design of the generalized sequential probability ratio test

A generalized sequential probability ratio test (GSPRT) is a classical algorithm for binary sequential hypothesis testing. Though it is well-studied in the literature, there has been no optimal design of this test due to the difficulty of choosing its thresholds. In this paper we formulate the binary sequential hypothesis testing as an optimization problem. The latter is non-convex, and finding a global minimizer of the objective is combinatorially complex in the number of stages of the sequential test. On the other hand, greedily minimizing the objective has linear complexity but achieves sub-optimal results. We propose a generalization of the greedy approach that allows the designer to trade off complexity for closeness of the thresholds to their optimal values. Simulation results show that the proposed method gives arbitrarily close solution to optimal by increasing the window span of future sample distributions that are utilized to set a current test threshold. The window span is a hyper-parameter that is optimized for the target application.
Convolutional Decoding in the Presence of Synchronization Errors

We describe the operation of common convolutional decoding algorithms in the presence of insertions, deletions, as well as substitutions in the received message. We first propose a trellis description that can handle the existence of insertions and deletions. Then, we use this trellis diagram to develop the Viterbi algorithm and the Log-MAP algorithm in the presence of synchronization errors. The proposed techniques are presented in the most general form where standard convolutional codes are used and no change to the encoder is required. We establish the effectiveness of the proposed algorithms using standard convolutional codes at different rates.
Image transmission over error-prone channels: sigma filtering and multiple description objectives

This work considers a fundamental problem of image transmission over error-prone channels. The impairments that we target are long burst errors and transient channel failures, as would occur in the wireless network because of an obstruction in the transmission path, or severe traffic congestion in a packet network such as the Internet. These impairments cause many packets to be dropped. In contrast to the traditional schemes that first introduce some amount of redundancy among descriptions, and then use this correlation for combating channel impairments, we investigate a new scheme to tackle this problem. The proposed scheme is to capture the most important visual features of a given image, extracted by sigma filtering preprocessing, into independent equal length packets. At the receiver side, we adopt a smart interpolation-based technique. The reconstruction quality at the receiver depends only on the number of packets received, but is independent of the place from where they were cropped. The preliminary results on standard test images show that our reconstructed image is very pleasant to human eyes, and achieves reasonable PSNR values, while at the same time using a lower bit rate than other reported traditional schemes based on multiple description coding.
Classification of Motor Imagery EEG recordings with Subject Specific Time-Frequency Patterns

We introduce an adaptive time-frequency plane feature extraction and classification system for the classification of motor imagery EEG recordings in a Brain Computer Interface task. First the EEG is segmented in time axis with a merge/divide strategy. This is followed by a clustering procedure in the frequency domain in each selected time segment to choose the most discriminant frequency features. The resulting adaptively selected time-frequency features are processed by principal component analysis-PCA for dimension reduction and fed to a linear discriminant classifier. The algorithm was applied to all nine subjects of the 2002 BCI competition. The classification performance of our proposed algorithm varied between 70% and 92.6% for each subject, which gives an average classification accuracy of 80.6%. The algorithm outperformed the reference standard adaptive autoregressive model based classification procedure for all subjects. This latter approach had an average error rate of %76.3 on the same subjects. We observed that the time-frequency tiling selected by the algorithm for EEG signal classification differs from subject to subject. Furthermore, the two hemispheres of the same subject are represented by distinct time-frequency segmentations and features. We argue that the method can adapt automatically to physio-anatomical differences and subject specific motor imagery patterns
Balanced Binary Sequences for UWB OFDM Systems

Orthogonal frequency division multiplexing (OFDM) based ultra-wideband (UWB) communication systems are becoming industry standard. In our recent work, we have found that the coding gain of a preceded UWB OFDM system can be maximized if the out-of-phase autocorrelation of the encoding sequence is zero for initial lags. In this paper, we consider perfectly balanced binary encoding sequences. Our simulation results show that sequences with coding gain close to its maximum value produce comparable results. For shorter length sequences, we use exhaustive computer search to find sequences with best possible autocorrelation. We also propose different designs for larger length sequences.