nils-herrmann/clean_scirepeval huggingface dataset:
Anti-cheating prosumer energy exchange based on indirect reciprocity

Equipped with renewable energy generators, pro-sumers produce, store and consume energy and become an important entity in smart grids. With time-variant power demands and generation capacities, prosumers can exchange energy via local power lines with less transmission loss than the long-distance power lines to the traditional power plants. In this paper, we develop a local power exchange game and address the cheating problem that seller prosumers actually supply insufficient energy instead of the promised amount in the trade. We apply the indirect reciprocity principle to build an anti-cheating local energy exchange system that maintains reputations for the prosumers according to their selling histories. By supplying less energy to the buyers who cheated in previous trades, this system motivates the autonomous prosumers to cooperate instead of cheating. Simulation results have shown that this strategy significantly reduces the number of cheating prosumers and improves the overall system utility. Compared with the direct reciprocity-based counterpart, this system reaches the desirable equilibrium much faster and requires less energy from the traditional energy plants.
Evolutionary Dynamics of Information Diffusion Over Social Networks

Current social networks are of extremely large-scale generating tremendous information flows at every moment. How information diffuses over social networks has attracted much attention from both industry and academics. Most of the existing works on information diffusion analysis are based on machine learning methods focusing on social network structure analysis and empirical data mining. However, the network users' decisions, actions, and socio-economic interactions are generally ignored by most of existing works. In this paper, we propose an evolutionary game theoretic framework to model the dynamic information diffusion process in social networks. Specifically, we derive the information diffusion dynamics in complete networks, uniform degree, and nonuniform degree networks, with the highlight of two special networks, the Erdös-Rényi random network and the Barabási-Albert scale-free network. We find that the dynamics of information diffusion over these three kinds of networks are scale-free and all the three dynamics are same with each other when the network scale is sufficiently large. To verify our theoretical analysis, we perform simulations for the information diffusion over synthetic networks and real-world Facebook networks. Moreover, we also conduct an experiment on a Twitter hashtags dataset, which shows that the proposed game theoretic model can well fit and predict the information diffusion over real social networks.
Behavior forensics with side information for multimedia fingerprinting social networks

In multimedia social networks, there exists complicated dynamics among users who share and exchange multimedia content. Using multimedia fingerprinting as an example, this paper investigates the human behavior dynamics in the multimedia social networks with side information. Side information is the information other than the colluded multimedia content that can help increase the probability of detection. We study the impact of side information in multimedia fingerprinting and show that the statistical means of the detection statistics can help the fingerprint detector significantly improve the collusion resistance. We then investigate how to probe the side information and model the dynamics between the fingerprint detector and the colluders as a two-stage extensive game with perfect information. We model the colluder-detector behavior dynamics as a two-stage game and find the equilibrium of the colluder-detector game using backward induction and show that the min-max solution is a Nash equilibrium, which gives no incentive for everyone in the multimedia fingerprint social network to deviate. This paper demonstrates that the proposed side information can significantly help improve the system performance to almost the same as the optimal correlation-based detector. Such result opens up a new scope in the research of fingerprinting system that given any fingerprint code, leveraging side information can improve the collusion resistance. Also, we provide the solutions to how to reach optimal collusion strategy and the corresponding detection, thus lead to a better protection of the multimedia content.
Fractal modeling and segmentation for the enhancement of microcalcifications in digital mammograms

The objective of this research is to model the mammographic parenchymal and ductal patterns and enhance the microcalcifications using a deterministic fractal approach. According to the theory of deterministic fractal geometry, images can be modeled by deterministic fractal objects which are attractors of sets of two-dimensional (2-D) affine transformations. The iterated functions systems and the collage theorem are the mathematical foundations of fractal image modeling. Here, a methodology based on fractal image modeling is developed to analyze and model breast background structures. The authors show that general mammographic parenchymal and ductal patterns can be well modeled by a set of parameters of affine transformations. Therefore, microcalcifications can be enhanced by taking the difference between the original image and the modeled image. The authors' results are compared with those of the partial wavelet reconstruction and morphological operation approaches. The results demonstrate that the fractal modeling method is an effective way to enhance microcalcifications. It may also be able to improve the detection and classification of microcalcifications in a computer-aided diagnosis system.
Toward a unified framework for modeling and analysis of diversity in joint source-channel coding

The study of joint source-channel coding (JSCC) systems faces one major challenge in obtaining an analytical expression for the function that links end-to-end distortion with channel signal-to-noise ratio, the D-SNR curve. In this paper, for certain multimedia systems using practical source and channel codes in a JSCC bit rate allocation design, the D-SNR curve is shown to be well approximated by a set of carefully selected points where the relative contribution of channel errors to end-to-end distortion is small. This approach has the potential advantage that it could be applied to represent performance of many practical systems using JSCC bit rate allocation for which it is shown that the D-SNR function is approximately linear in log-log scales. A unified framework for the modeling, analysis and performance measurement of these systems is proposed by considering a view of diversity more general than its usual interpretation. This view extends that of diversity to include redundant information so coding and diversity gain are still used to characterize performance. Furthermore, the proposed approach is applied to study issues arising from using practical source and channel codes, including the effects on performance of channel codes of different strength or source codes with different compression efficiency.
Transmit beamforming for space-frequency coded MIMO-OFDM systems with spatial correlation feedback

This paper addresses the problem of joint optimization of transmit beamforming and space-frequency (SF) coding for MIMO-OFDM systems with spatial correlation feedback in broadband communications. This problem is challenging in the sense that the transmitter should be designed to beamform across multiple eigenspaces associated with the multipath environment simultaneously. With arbitrary transmit spatial correlation, the performance analysis for SF-coded MIMO-OFDM systems with beamforming is provided, and a general optimization problem for the beamforming design is formulated. Three suboptimal approaches to design the beamformer based on the derived design criteria are proposed: i) eigenvalue selection scheme; ii) eigenspace selection scheme; and iii) per-subcarrier approach based on decoding at each subcarrier. The proposed schemes take into account the multiple eigenspace information associated with the multipath-delay channel. Improvement in the performance over SF coding without beamforming is shown through simulations in terms of bit error rate. The eigenvalue selection scheme provides the best performance among the proposed algorithms. This scheme locates the subspace associated with the largest eigenvalues in the eigenspace of the covariance matrices. With the eigenvalue selection scheme, the performance improvement is about 3 dB over the SF coding without beamforming for highly correlated channels as shown in our simulations.
Split Recursive Least-Squares: algorithms, architectures, and applications

In this paper, a new computationally efficient algorithm for adaptive filtering is presented. The proposed Split Recursive Least-Squares (Split RLS) algorithm can perform the approximated RLS with O(N) complexity for signals having no special data structure to be exploited (e.g., the signals in multichannel adaptive filtering applications, which are not shifts of a single-channel signal data), while avoiding the high computational complexity (O(N/sup 2/)) required in the conventional RLS algorithms. Our performance analysis shows that the estimation bias will be small when the input data are less correlated. We also show that for highly correlated data, the orthogonal preprocessing scheme can be used to improve the performance of the Split RLS. Furthermore, the systolic implementation of our algorithm based on the QR-decomposition RLS (QRD-RLS) array as well as its application to multidimensional adaptive filtering is also discussed. The hardware complexity for the resulting array is only O(N) and the system latency can be reduced to O(log/sub 2/ N). The simulation results show that the Split RLS outperforms the conventional RLS in the application of image restoration. A major advantage of the Split RLS is its superior tracking capability over the conventional RLS under nonstationary environments.
Diversity analysis for frequency-selective MIMO-OFDM systems with general spatial and temporal correlation model

In this paper, the effect of a general spatial and temporal fading correlation structure on the performance of coded multiple-input multiple-output (MIMO)-orthogonal frequency-division multiplexing (OFDM) systems is studied. The analysis handles an arbitrary joint transmit-receive spatial correlation model, including the non-Kronecker model. An upper bound on the maximum achievable diversity order for frequency-selective MIMO-OFDM systems with general temporal and spatial correlation is derived. Furthermore, a space-time-frequency code design that can achieve the upper bound for any arbitrarily correlated channel scenario is provided. The general framework of the analysis includes space-frequency (SF)-coded systems as a special case. For the SF-coded MIMO-OFDM system, it is shown that any SF code designed to achieve full diversity in the independent fading channel can achieve full diversity in an arbitrary spatially correlated channel. The derived analytical results are consistent with those in the existing literature for special correlation structures. Extensive simulation results are provided to confirm the theoretical analysis.
COGNITIVE RADIOS FOR DYNAMIC SPECTRUM ACCESS - Dynamic Spectrum Sharing: A Game Theoretical Overview

In order to fully utilize the scarce spectrum resources, with the development of cognitive radio technologies, dynamic spectrum sharing becomes a promising approach to increase the efficiency of spectrum usage. Game theoretical dynamic spectrum sharing has been extensively studied for more flexible, efficient, and fair spectrum usage through analyzing the intelligent behaviors of network users equipped with cognitive radio devices. This article provides a game theoretical overview of dynamic spectrum sharing from several aspects: analysis of network users' behaviors, efficient dynamic distributed design, and optimality analysis
Spectral estimation based on structured low rank matrix pencil

This paper proposes a new parameter estimation algorithm for damped sinusoidal signals. Parameter estimation for damped sinusoidal signals with additive white noise is a problem of significant interest in many signal processing applications, like analysis of NMR data and system identification. The new algorithm estimates the signal parameters using a matrix pencil constructed from the measured data. To reduce the noise effect, a rank deficient Hankel approximation of the prediction matrix is used. The performance of the new algorithm is significantly improved by structured low rank approximation of the prediction matrix. Computer simulations show that the noise threshold of the new algorithm is significantly better than the existing algorithms.
Performance characterization of multiband UWB communication systems using Poisson cluster arriving fading paths

This paper provides a novel performance analysis for ultra-wideband (UWB) systems that successfully captures the unique multipath-rich property and multipath-clustering phenomenon of UWB channels. Using the Saleh-Valenzuela model, we characterize pairwise error probability and outage probability for UWB systems employing multiband OFDM based on the cluster arrival rate, the ray arrival rate within a cluster, and the cluster and ray decay factors. Furthermore, an approximation technique is established, which allows us to obtain closed-form performance formulations that provide insightful understanding of the effect of channel characteristics on the performances of UWB systems. Finally, we characterize the effect of random-clustering phenomenon on the performance of UWB-multiple-input-multiple-output systems. The theoretical results reveal that regardless of the clustering behavior of UWB channels, the diversity gain can be improved by increasing the number of jointly encoded subcarriers, the number of jointly encoded orthogonal frequency-division multiplexing symbols, or the number of antennas. The coding gain on the other hand, depends heavily on the cluster-arriving channels. Extensive simulation results are provided to support the theoretical analysis.
Mitigating channel estimation error with timing synchronization tradeoff in cooperative communications

Channel estimation error and cochannel interference (CCI) problems are among the main causes of performance degradation in wireless networks. In this paper, we investigate the impact of cooperative communications on mitigating the effect of channel estimation error and CCI. Two main performance criteria, namely, the traditional outage probability and the proposed signal-to-noise ratio (SNR) gap ratio, are utilized to characterize such impact. The SNR gap ratio measures the reduction in the SNR due to channel estimation error or CCI. Taking into consideration the channel estimation error, we show that the outage probability is reduced by utilizing cooperative transmission protocols. We also show that cooperative transmission scenarios, in which each cooperating relay forwards its signal over an orthogonal channel, result in lower SNR gap ratio compared to that of the direct transmission. Thus, cooperative transmission schemes are less susceptible to the effect of channel estimation error compared to direct transmission. Moreover, increasing the number of cooperating relays reduces the effect of the channel estimation error more. Timing synchronization error arises in distributed space-time cooperative schemes, in which the cooperating relays are simultaneously transmitting their signals over the same channel. Unlike the channel estimation error, the effect of the timing synchronization error gets worse as the the number of cooperating relays increases. In this work we also study the tradeoff between the timing synchronization error and the channel estimation error, and show their net impact on the system performance. Finally, we illustrate that CCI can be modeled in a similar fashion to the channel estimation error, and hence the cooperative transmission schemes are also less susceptible to the effect of CCI.
Toward a Unified Framework for Modeling and Analysis of Diversity in Joint Source--Channel Coding

The study of joint source-channel Coding (JSCC) systems faces one major challenge in obtaining an analytical expression for the function that links end-to-end distortion with channel signal-to-noise ratio, the D-SNR curve. In this paper, for certain multimedia systems using practical source and channel codes in a JSCC bit rate allocation design, the D-SNR curve is shown to be well approximated by a set of carefully selected points where the relative contribution of channel errors to end-to-end distortion is small. This approach has the potential advantage that it could be applied to represent the performance of many practical systems using JSCC bit rate allocation for which it is shown that the D-SNR function is approximately linear in log-log scales. A unified framework for the modeling, analysis, and performance measurement of these systems is proposed by considering a view of diversity more general than its usual interpretation. This view extends that of diversity to include redundant information so that coding and diversity gain are still used to characterize performance. Furthermore, the proposed approach is applied to study issues arising from using practical source and channel codes, including the effects on the performance of channel codes of different strength or source codes with different compression efficiency.
Forensic detection of image manipulation using statistical intrinsic fingerprints

As the use of digital images has increased, so has the means and the incentive to create digital image forgeries. Accordingly, there is a great need for digital image forensic techniques capable of detecting image alterations and forged images. A number of image processing operations, such as histogram equalization or gamma correction, are equivalent to pixel value mappings. In this paper, we show that pixel value mappings leave behind statistical traces, which we shall refer to as a mapping's intrinsic fingerprint, in an image's pixel value histogram. We then propose forensic methods for detecting general forms globally and locally applied contrast enhancement as well as a method for identifying the use of histogram equalization by searching for the identifying features of each operation's intrinsic fingerprint. Additionally, we propose a method to detect the global addition of noise to a previously JPEG-compressed image by observing that the intrinsic fingerprint of a specific mapping will be altered if it is applied to an image's pixel values after the addition of noise. Through a number of simulations, we test the efficacy of each proposed forensic technique. Our simulation results show that aside from exceptional cases, all of our detection methods are able to correctly detect the use of their designated image processing operation with a probability of 99% given a false alarm probability of 7% or less.
Optimal Defense against Jamming Attacks in Cognitive Radio Networks Using the Markov Decision Process Approach

Cognitive radio technology has become a promising approach to increase the efficiency of spectrum utilization. Since cognitive radio users are vulnerable to malicious attacks, security countermeasures are crucial to the successful deployment of cognitive radio networks in the future. In this paper, we focus on defending against the jamming attack, one of the major threats to cognitive radio networks, where several malicious attackers intend to jam the secondary user's communication link by injecting interference. We model this scenario into a jamming game, and derive the optimal strategy through the Markov decision process approach. Furthermore, a learning scheme is proposed for the secondary user to observe the wireless environment and estimate parameters such as primary users' access pattern and the number of attackers. Finally, simulation results are presented to verify the performance.
Multi-person breathing rate estimation using time-reversal on WiFi platforms

In this paper, we present TR-BREATH, a time-reversal (TR) based, contact-free, accurate breathing monitoring system capable of multi-person breathing rate estimation within a short period of time (e.g., around one minute) using off-the-shelf WiFi devices. TR-BREATH exploits the fine-grained channel state information (CSI) on WiFi devices to capture the minor variations caused by breathing. To amplify such variations, TR-BREATH projects CSI time series into TR resonating strength (TRRS) feature space and performs Root-MUSIC to extract candidates of breathing rates. Then, TR-BREATH performs affinity propagation to partition these candidates into clusters corresponding to the breathing of different people. Extensive experiment results in a typical indoor environment demonstrate that under an non-line-of-sight scenario, TR-BREATH achieves an accuracy of 98.5% in estimating the breathing rates of one person, and a mean accuracy of 96.9% averaging over the accuracies of 1 to 7 people.
Optimal pricing in stochastic scalable video coding multicasting system

Heterogeneous multimedia content delivery over wireless networks is an important yet challenging issue. A promising solution is combining multicasting and scalable video coding (SVC) techniques via cross-layer design which has been shown to be effectively solution in the literature. Nevertheless, most existing works on SVC multicasting system focus on the static scenarios. In addition, the economic value of SVC multicasting system has seldom been explored. In this work, we study a subscription-based SVC multicasting system with stochastic user arrival and heterogeneous user preferences. A stochastic framework based on Multi-dimensional Markov Decision Process (M-MDP) is proposed to study the negative network externality existing in the proposed system. A game-theoretic analysis is conducted to understand the rational demands from heterogeneous users the subscription economic model. We show that the optimal pricing strategy which maximizes the expected revenue of the service provider can be derived through dynamic iterative updating techniques. Moreover, the overall user's valuation on the system is maximized under such an optimal pricing strategy. Finally, the solution efficiency is evaluated through simulations.
A unified square-root-free approach for QRD-based recursive-least-squares estimation

The generic formula for the Givens rotation, the most commonly used method for QR-decomposition (QRD) updating, requires explicit square-root (sqrt) computations, which constitute a computational bottleneck. A unified systematic approach for sqrt-free Givens rotations is provided. By properly choosing two parameters, all previously known, as well as new, sqrt-free methods are included in the unified approach. This unified treatment is also extended to the QRD-based recursive-least-squares (RLS) problem for optimum residual acquisition without sqrt operations.