nils-herrmann/clean_scirepeval huggingface dataset:
DCT-based subpixel motion estimation

Inter-pixel interpolation is required for most subpixel motion estimation schemes but it undesirably increases the overall complexity and data flow and deteriorates the estimation accuracy. We develop DCT-based techniques to estimate subpel motion at different desired levels of accuracy in the DCT domain without interpolation by establishing subpel sinusoidal orthogonal principles and showing that subpixel motion information is preserved in the DCT of a shifted signal under some conditions in the form of pseudo phases. Though applicable to other areas, the resulting algorithms from these techniques for video coding are flexible and scalable with very low complexity O(N/sup 2/) compared to O(N/sup 4/) for block matching methods. Importantly, DCT-based methods enable simplification of the heavily loaded feedback loop of conventional hybrid video coder design, resulting in a high-throughput, low-complexity fully DCT-based coder. Finally, simulation results show a comparable performance of the proposed algorithms with block matching methods.
Multiuser rate allocation games for multimedia communications

How to efficiently and fairly allocate data rate among different users is a key problem in the field of multiuser multimedia communication. However, most of the existing optimization-based methods, such as minimizing the weighted sum of the distortions or maximizing the weighted sum of the peak signal-to-noise ratios (PSNRs), have their weights heuristically determined. Moreover, those approaches mainly focus on the efficiency issue while there is no notion of fairness. In this paper, we address this problem by proposing a game-theoretic framework, in which the utility/payoff function of each user/player is jointly determined by the characteristics of the transmitted video sequence and the allocated bit-rate. We show that a unique Nash equilibrium (NE), which is proportionally fair in terms of both utility and PSNR, can be obtained, according to which the controller can efficiently and fairly allocate the available network bandwidth to the users. Moreover, we propose a distributed cheat-proof rate allocation scheme for the users to converge to the optimal NE using alternative ascending clock auction. We also show that the traditional optimization-based approach that maximizes the weighted sum of the PSNRs is a special case of the game-theoretic framework with the utility function defined as an exponential function of PSNR. Finally, we show several experimental results on real video data to demonstrate the efficiency and effectiveness of the proposed method.
Information theoretical limit of compression forensics

Multimedia forensics concerns on extracting forensic information from suspicious multimedia contents. This information was embedded into the content inadvertently whenever an operation happened. Investigators may estimate the possible operations by obtaining features from the multimedia content and applying detection algorithms based on the statistics. While most existing works focus on improving detection performance and finding what more we can do, understanding the fundamental limit on the forensic information that we can obtain from the extracted features is also important. It enables us to understand the limit of forensicability. In this paper, we explore the fundamental limit of forensicability by introducing an information theoretical framework for multimedia forensics. We use mutual information as the measure of forensic information conveyed by features to investigators. To show the analytical process, we take the case of multiple JPEG compression forensics as an example. We claim that, under typical circumstances, the maximum number of compressions that we can detect by examine DCT coefficients is up to 4, in an expected sense. In addition, we also find the patterns of compression quality factors that contain the most and least forensic information.
A residual-based selective window for robust recursive least squares estimation

An algorithm performing recursive least squares (RLS) estimation is proposed. It is based on selectively rejecting outliers arising from noise spikes; therefore, this method can avoid the bias of parameters estimation due to some large noise perturbations. Unlike a sliding fixed-window scheme, this windowing scheme can be noncontinuous. It depends on the estimated level of observed errors (residual). By monitoring the residuals in a recursive manner, one can effectively remove those spurious observed data by downdating them. The proposed scheme is useful, especially when some short-time large interferences perturb the system occasionally. In this respect, it outperforms existing schemes, either exponentially growing or sliding window.<<ETX>>
Secure Cooperation in Autonomous Mobile Ad-Hoc Networks Under Noise and Imperfect Monitoring: A Game-Theoretic Approach

In autonomous mobile ad-hoc networks, one major challenge is to stimulate cooperation among selfish nodes, especially when some nodes may be malicious. In this paper, we address cooperation stimulation in realistic yet challenging contexts where the environment is noisy and the underlying monitoring is imperfect. We have first explored the underlying reasons why stimulating cooperation under such scenarios is difficult. Instead of trying to enforce all nodes to act fully cooperatively, our goal is to stimulate cooperation in a hostile environment as much as possible through playing conditional altruism. To formally address the problem, we have modeled the interactions among nodes as secure routing and packet forwarding games under noise and imperfect observation, and devised a set of reputation-based attack-resistant cooperation strategies without requiring any tamper-proof hardware or central banking service. The performance of the devised strategies has also been evaluated analytically. The limitations of the game-theoretic approaches and the practicability of the devised strategies have also been investigated through theoretical analysis and extensive simulation studies. The results have demonstrated that although sometimes a gap may exist between the ideal game model and the reality, game-theoretic analysis can still provide thoughtful insights and useful guidelines when designing cooperation strategies.
Transmit diversity techniques for multicasting over wireless networks

Transmit diversity (TD) is one of the key technologies to achieve high data rate communications in wireless fading environments. While transmit diversity techniques have been extensively studied for point-to-point communications, their applications in wireless multicast scenario have not been fully exploited. In this paper, we first design an adaptive transmit antenna array for multicasting, with the assumption of perfect channel information at the transmitter and the performance criteria as maximizing the worst signal-to-noise ratio (SNR) among all receivers. Compared with the existing beamforming scheme that aims to maximize the average SNR, a performance gain in decoding bit error rate is obtained. Then, the proposed close-loop scheme is compared with space-time block coding that does not require feedback of channel information. When the number of multicast receivers is below a certain threshold, the proposed close-loop scheme outperforms the space-time codes. When the multicast group size is large, the space-time codes have better performance. Finally, jointly considering close loop and open loop techniques based on group size and availability of channel information feedback is suggested.
Game-Theoretic Cross Social Media Analytic: How Yelp Ratings Affect Deal Selection on Groupon?

Deal selection on Groupon is a typical social learning and decision making process, where the quality of a deal is usually unknown to the customers. The customers must acquire this knowledge through social learning from other social medias such as reviews on Yelp. Additionally, the quality of a deal depends on both the state of the vendor and decisions of other customers on Groupon. How social learning and network externality affect the decisions of customers in deal selection on Groupon is our main interest. We develop a data-driven game-theoretic framework to understand the rational deal selection behaviors cross social medias. The sufficient condition of the Nash equilibrium is identified. A value-iteration algorithm is proposed to find the optimal deal selection strategy. We conduct a year-long experiment to trace the competitions among deals on Groupon and the corresponding Yelp ratings. We utilize the dataset to analyze the deal selection game with realistic settings. Finally, the performance of the proposed social learning framework is evaluated with real data. The results suggest that customers do make decisions in a rational way instead of following naive strategies, and there is still room to improve their decisions with assistance from the proposed framework.
Estimation of multiple sinusoidal frequencies using truncated least squares methods

To reduce the computational complexity of resolving closely spaced frequencies, three truncated QR methods are proposed: (1) truncated QR without column pivoting (TQR): (2) truncated QR with reordered columns) TQRR); and (3) truncated QR with column pivoting (TQRP). It is demonstrated that many of the benefits of the singular value decomposition based methods are achievable under the truncated QR methods with much lower computational cost. Based on the forward-backward linear prediction model, computer simulations and comparisons are provided for different truncation methods under various SNRs. Comparisons of asymptotic performance for large data samples are also given.
GlobalSIP: A New Flagship Conference [President's Message]

In order to solve various planning and administrative issues associated with conference workshops the idea of the IEEE Global Conference on Signal and Information Processing (GlobalSIP) as a new symposia-based fall flagship conference was born - a conference essentially with a collection of colocated symposia. Each symposium has its own technical chairs and review processes. The symposia are tied together through colocation, cotiming, a shared plenary, nonoverlapping oral lecture sessions, parallel poster sessions, and a single registration fee. The first GlobalSIP will be held 3-5 December 2013 in Austin, Texas, organized by a team led by Ahmed Tewfik and Robert Heath. There are several benefits to having multiple colocated symposia. The first benefit is the cross-fertilization of research ideas across the research community. Having broad oral sessions and interactive poster sessions in each symposium allows researchers in related fields to gain exposure to new ideas. Second, the symposia format will attract top talent in related fields that are outside our community. The symposia chairs will have the flexibility to design the symposium to match the needs of their community.
Signal processing techniques in network-aided positioning: a survey of state-of-the-art positioning designs

Wireless positioning has attracted much research attention and has become increasingly important in recent years. Wireless positioning has been found very useful for other applications besides E911 service, ranging from vehicle navigation and network optimization to resource management and automated billing. Although many positioning devices and services are currently available, it is necessary to develop an integrated and seamless positioning platform to provide a uniform solution for different network configurations. This article surveys the state-of-the-art positioning designs, focusing specifically on signal processing techniques in network-aided positioning. It serves as a tutorial for researchers and engineers interested in this rapidly growing field. It also provides new directions for future research for those who have been working in this field for many years.
MIMO-OFDM channel estimation via probabilistic data association based TOAs

The multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) is one of the most promising techniques to achieve broadband wireless communications. Channel estimation is critical in the design of MIMO OFDM systems, since channel parameters are required for coherent detection and decoding. In this work, we propose a channel estimation scheme based on the time of arrivals (TOAs) estimation. The TOAs are first determined using a variant of probabilistic data association (PDA) and employing the minimum description length principle, then the PDA is augmented by group decision feedback to refine the TOA estimates. The proposed algorithm is compared with the Fourier transform based method and the alternating projection (AP) algorithm. Simulation results show that the proposed scheme provides the performance comparable to that of AP and much better than that of the Fourier based method, meanwhile it requires much lower computational cost than that of AP.
A wavefront array for URV decomposition updating

The rank revealing URV decomposition is a useful tool in many signal processing applications that require the computation of the noise subspace of a matrix. In this paper, we look at the problem of updating the URV decomposition and consider its implementation on a wavefront array. We propose a wavefront array for efficient VLSI implementation of the URV decomposition. The array provides a mechanism for accumulating the null space of the matrix as well as for handling rank changes during updating.
The Promise of Radio Analytics: A Future Paradigm of Wireless Positioning, Tracking, and Sensing

With the proliferation of Internet of Things (IoT) applications, billions of household appliances, phones, smart devices, security systems, environment sensors, vehicles, buildings, and other radio-connected devices will transmit data and communicate with each other or people, and it will be possible to constantly measure and track virtually everything. Among the various approaches to measuring what is happening in the surrounding environment, wireless sensing has received increasing attention in recent years because of the ubiquitous deployment of wireless radio devices. In addition, human activities affect wireless signal propagation, so understanding and analyzing how these signals react to human activities can reveal rich information about the activities around us.
A class of square root and division free algorithms and architectures for QRD-based adaptive signal processing

The least squares (LS) minimization problem constitutes the core of many real-time signal processing problems, such as adaptive filtering, system identification and adaptive beamforming. Recently efficient implementations of the recursive least squares (RLS) algorithm and the constrained recursive least squares (CRLS) algorithm based on the numerically stable QR decomposition (QRD) have been of great interest. Several papers have proposed modifications to the rotation algorithm that circumvent the square root operations and minimize the number of divisions that are involved in the Givens rotation. It has also been shown that all the known square root free algorithms are instances of one parametric algorithm. Recently, a square root free and division free algorithm has also been proposed. In this paper, we propose a family of square root and division free algorithms and examine its relationship with the square root free parametric family. We choose a specific instance for each one of the two parametric algorithms and make a comparative study of the systolic structures based on these two instances, as well as the standard Givens rotation. We consider the architectures for both the optimal residual computation and the optimal weight vector extraction. The dynamic range of the newly proposed algorithm for QRD-RLS optimal residual computation and the wordlength lower bounds that guarantee no overflow are presented. The numerical stability of the algorithm is also considered. A number of obscure points relevant to the realization of the QRD-RLS and the QRD-CRLS algorithms are clarified. Some systolic structures that are described in this paper are very promising, since they require less computational complexity (in various aspects) than the structures known to date and they make the VLSI implementation easier.
On the robustness of space-time coding for spatially and temporally correlated wireless channels

The robustness of space-time coding techniques for wireless channels that exhibit both temporal and spatial correlation is investigated. A general space-time covariance model is developed and employed to evaluate the exact pairwise error probability for several space-time codes. A significant degradation in the performance of space-time coding techniques is observed for cases where the scatterers are located in close proximity to the mobile and the spacing between transmit antennas is a fraction of a wavelength. The conditions for which the commonly used assumption of independent transmission paths is valid are investigated as a function of the scattering radius and the spacing of the transmit and receive antennas.
Stochastic model and probabilistic decision-based classifier for mass detection in digital mammography

We have developed a combined method utilizing morphological operations, a finite generalized Gaussian mixture (FGGM) modeling, and a contextual Bayesian relaxation labeling technique (CBRL) to enhance and extract suspicious masses. A feature space is constructed based on multiple feature extraction from the regions of interest (ROIs). Finally, a multi-modular probabilistic decision-based classifier is employed to distinguish true masses from non-masses.
A time-reversal spatial hardening effect for indoor speed estimation

Time-reversal (TR) transmission scheme has attracted more and more attention from both academia and industry due to its ability to focus the energy of a transmitted signal at an intended focal spot, both in the time and spatial domains. Based on the extensive data collected in the real world, we observe that the energy distribution around the focal spot is highly stationary and location-independent, which we call as the “TR spatial hardening effect”. This is because TR scheme adds up numerous copies of the transmitted signal bouncing off different scatterers coherently and the randomness from the environment is averaged out. We characterize the statistical behaviors of the energy distribution around the focal spot using a statistical model. By exploiting the hardening effect, the moving speed of transceivers can be estimated in indoor environments and extensive experiments show the superiority of the proposed method compared with previous works using RF signals.
Enabling Heterogeneous Connectivity in Internet of Things: A Time-Reversal Approach

With the pervasive presence of massive smart devices, Internet of Things (IoT) is enabled by wireless communication technology. The devices in IoT usually have very diverse bandwidth capabilities and thus in need of many communication standards. To facilitate communications between these heterogeneous bandwidths of devices, middlewares have often been developed. However, they are often not suitable for resource-constrained scenario due to their complexity. It leads us to ask is there a unified approach that can support the communication between the devices with heterogeneous bandwidths? In this paper, we propose the time-reversal (TR) approach to answer such a question. A novel TR-based heterogeneous system is proposed, which can address the bandwidth heterogeneity and maintain the benefit of TR at the same time. Although there is an increase in complexity, it concentrates mostly on the digital processing of the access point (AP), which can be easily handled with more powerful digital signal processor (DSP). Since there is no middleware in the proposed system and the additional physical layer complexity concentrates on the AP side, the proposed TR approach better satisfies the requirement of low complexity and energy efficiency for terminal devices (TDs). We further conduct the theoretical analysis of the interference in the proposed system. Simulations show the bit-error-rate (BER) performance can be significantly improved with appropriate spectrum allocation. Finally, Smart Homes is chosen as an example of IoT applications to evaluate the performance of the proposed system.
Behavior modeling and forensics for multimedia social networks

Factors influencing human behavior have seldom appeared in signal processing disciplines. Therefore, the goals of the article are to illustrate why human factors are important, identify emerging issues strongly related to signal processing, and to demonstrate that signal processing can be effectively used to model, analyze, and perform behavior forensics for multimedia social networks. Since media security and content protection is a major issue, the article illustrates various aspects of issues and problems in multimedia social networks via a case study of human behavior in traitor-tracing multimedia fingerprinting. We focus on the understanding of behavior forensics from signal processing perspective and present a framework to model and analyze user dynamics. The objective is to provide a broad overview of recent advances in behavior modeling and forensics for multimedia social networks.
Best-Effort Cooperative Relaying

Traditional cooperative communications consider dedicated-relays, while often such relays may not be available. In this paper, we consider wireless transceivers that relay signals in addition to their own primary communication mission. We consider a best-effort delivery policy, where a node is not obligated to devote energy to cooperatively relay signals, nor does it provide a guarantee of signal quality on retransmissions. Instead the relay sacrifices energy at its own discretion, with priority given to the primary communication mission. We consider one best-effort delivery problem: a system that transmits an additional relay signal within its original transmission energy budget while inducing minimal degradation to the primary-user's signal. To maintain this constraint, we consider the feasibility of reallocating energy from pilot signals used for channel estimation toward the relaying service, when channel conditions are stationary. We demonstrate that transmitter energy may be dynamically allocated between a relay component and a pilot component of the transmission using best-effort delivery. This power allocation is critical to system performance, since both the primary-user and the secondary-user may require pilot energy to correctly decode transmitted signals. Sub-optimal power allocation rules with respect to primary-user channel estimate mean-square error and pairwise error probability are derived.
Forensic analysis of nonlinear collusion attacks for multimedia fingerprinting

Digital fingerprinting is a technology for tracing the distribution of multimedia content and protecting them from unauthorized redistribution. Unique identification information is embedded into each distributed copy of multimedia signal and serves as a digital fingerprint. Collusion attack is a cost-effective attack against digital fingerprinting, where colluders combine several copies with the same content but different fingerprints to remove or attenuate the original fingerprints. In this paper, we investigate the average collusion attack and several basic nonlinear collusions on independent Gaussian fingerprints, and study their effectiveness and the impact on the perceptual quality. With unbounded Gaussian fingerprints, perceivable distortion may exist in the fingerprinted copies as well as the copies after the collusion attacks. In order to remove this perceptual distortion, we introduce bounded Gaussian-like fingerprints and study their performance under collusion attacks. We also study several commonly used detection statistics and analyze their performance under collusion attacks. We further propose a preprocessing technique of the extracted fingerprints specifically for collusion scenarios to improve the detection performance.