nils-herrmann/clean_scirepeval huggingface dataset:
Nonlinear filtering enhancement and histogram modeling segmentation of masses for digital mammograms

The objective of this study is to develop an efficient method to highlight the geometric characteristics of defined patterns, and isolate the suspicious regions which in turn provide the improved segmentation of objects. In this work, a combined method of using morphological operations, finite generalized Gaussian mixture modeling, and contextual Bayesian relaxation labeling was developed to enhance and segment various mammographic contexts and textures. This method was applied to segment suspicious masses on mammographic images. The testing results showed that the proposed method can detect all suspected masses as well as high contrast objects and can he used as an effective pre-processing step of mass detection with computer scheme.
A predictive H.263 bit-rate control scheme based on scene information

For many real-time visual communication applications such as video telephony, the transmission channel used is typically constant bit rate (e.g. PSTN). Under this circumstance, the encoder's output bit-rate must be regulated to meet the transmission bandwidth in order to guarantee a constant frame rate and delay. We propose a new predictive rate control scheme based on the activity of the scene to be coded for H.263 encoder.
Joint uplink and downlink optimization for video conferencing over wireless LAN

A real-time video conferencing framework is proposed for multiple conferencing pairs by jointly considering the uplink and downlink conditions within IEEE 802.11 networks. We formulate this system so as to minimize the maximal end-to-end expected distortion received by all users by selecting the PHY modes and transmission time. Compared with the strategy of individually optimizing uplink and downlink, the proposed framework outperforms by 3.67-8.65 dB for the average received PSNR among all users.
Collusion-Resistant Dynamic Spectrum Allocation for Wireless Networks via Pricing

Dynamic spectrum allocation becomes a promising approach to increase the spectrum efficiency for wireless networks. However, the collusion among selfish network users may seriously deteriorate the efficiency of dynamic spectrum sharing. In this paper, we propose a collusion-resistant dynamic pricing approach to optimize overall spectrum efficiency in the scenarios of user collusion. The simulation results show that our proposed scheme achieves high efficiency of spectrum usage even with the presence of severe user collusion.
Indian Buffet Game with non-Bayesian social learning

How users in a dynamic system perform learning and make decision become more and more important in numerous research fields. In this paper, we propose an Indian Buffet Game to study how users in a dynamic system learn the uncertain system state and make multiple concurrent decisions by not only considering the current utility, but also taking into account the influence of subsequent users' decisions. We analyze the proposed Indian Buffet Game under two different scenarios: one is customers has budget constraint and the other is without budget constraint. For both cases, we design recursive best response algorithms to find the subgame perfect Nash equilibrium for customers. Moreover, we introduce a non-Bayesian social learning algorithm for customers to learn the system state. Finally, we conduct simulations to validate the effectiveness and efficiency of the proposed algorithms.
Cognitive radio games

As our radios get smarter, they'll be competing for over-crowded airwaves. Game theory can make them cooperate. The cognitive-radio game the author sketched here is admittedly an idealization. Things rapidly become more complicated when you consider more realistic spectrum-sharing situations. For some frequency bands, for example, a primary licensee will be involved, in which case you need to figure in the value of the payments secondary users must make to the "owner" of the spectrum (or even to one another) in determining their motivations. Also, real cognitive radios are able to adjust in more ways than just by switching their transmissions on or off. They can be required to limit the amount of power they use to transmit or to shift to a protocol that sends less data but produces less interference for others. The variations are nearly endless.
Exploring Spatial Focusing Effect for Spectrum Sharing and Network Association

Next-generation wireless networks are expected to support an exponentially increasing number of users and demands of data, which all rely on the essential media: spectrum. Spectrum sharing among heterogeneous networks is a fundamental issue that determines network performance. Previous works have focused on a “dynamic spectrum access” mode associated with the cognitive radio technology. These works all focused on the discovery of available spectrum resource either in the time domain or in the frequency domain, i.e., by separating different users’ transmission. To initiate a new paradigm of spectrum sharing, the unique characteristics of the technologies should be utilized in the next-generation networks. The 5G networks are featured either by wide bandwidth like mm-wave systems, or by large-scale antennas like massive MIMO. Those two trends can lead to a common phenomenon: the spatial focusing effect. Based on this focusing effect, we propose a general spatial spectrum sharing framework that enables concurrent multi-users spectrum sharing without the requirement of orthogonal resource allocation. Moreover, we design two general network association protocols either in a centralized manner or in a distributed manner. Simulation results show that both the time reversal wideband and the massive MIMO system can achieve high throughput performance with the spatial spectrum sharing scheme.
Improving Connectivity via Relays Deployment in Wireless Sensor Networks

Enhancing the connectivity of wireless sensor networks is necessary to avoid the occurrence of coverage gaps. In this paper, we aim at improving the network connectivity of a given network by adding a set of relays to it. We characterize the network connectivity by the Fiedler value, which is the second smallest eigenvalue of the Laplacian matrix representing the network graph. We propose a network-maintenance algorithm, which finds the best locations for a given set of relays. The proposed algorithm obtains the best relays' locations through a multi-level approach. In each level, the search problem can be formulated as a standard semi-definite programming (SDP) optimization problem. We show that the proposed algorithm can increase the average Fiedler value by 35% by adding one relay only.
Pricing game and evolution dynamics for mobile video streaming

The recent developments of smart mobile phones and 3G networks enable users to enjoy video programs by subscribing to data plans. Due to phone-to-phone communication technologies and ubiquity of mobile phones, data-plan subscribers are able to redistribute the video content to non-subscribers. Such a redistribution mechanism is a potential competitor for the mobile service provider and is very difficult to trace given users' high mobility. The service provider has to set a reasonable price for the data plan to prevent such unauthorized re-distribution behavior to protect or maximize his/her own profit. In this paper, we investigate the evolutionarily stable ratio of mobile users who decide to subscribe to the data plan. Such an analysis can help the service provider preserve his/her profit under the threat of the redistribution networks and can improve the quality of service for end users.
Network Formation Games in Cooperative MIMO Interference Systems

This paper considers the cooperative optimization of mutual information in the MIMO Gaussian interference channel in a fully distributed manner via game theory. Null shaping constraints are enforced in the design of transmit covariance matrices to enable interference mitigation among links. The transmit covariance matrices leading to the Nash Equilibrium (NE) are derived, and the existence and uniqueness of the NE is analyzed. The formation of the cooperative sets, that represent the cooperation relationship among links, is considered as coalition games and network formation games. We prove that the proposed coalition formation (CF) and coalition graph formation (CGF) algorithms are Nash-stable, and the proposed network formation (NF) algorithm converges to a Nash Equilibrium. Simulation results show that the proposed CF and CGF algorithms have significant advantages when the antennas at the transmitters is large, and the proposed NF algorithm enhances the sum rate of the system apparently even at low signal-to-noise ratio region and/or with small number of transmit antennas.
Low-Power Architectures for Compressed Domain Video Coding Co-Processor

Low power as a de facto is one of the most important criteria for many signal-processing system designs, particularly in multimedia cellular applications and multimedia system on chip design. There have been many approaches to achieve this design goal at many different implementation levels ranging from very-large-scale-integration fabrication technology to system design. In this paper, the multirate low-power design technique will be used along with other methods such as look-ahead, pipelining in designing cost-effective low-power architectures of compressed domain video coding co-processor. Our emphasis is on optimizing power consumption by minimizing computational units along the data path. We demonstrate both low-power and high-speed can be accomplished at algorithm/architecture level. Based on the calculation and simulation results, the design can achieve significant power savings in the range of 60%-80% or speedup factor of two at the needs of users.
Fair multiuser channel allocation for OFDMA networks using Nash bargaining solutions and coalitions

In this paper, a fair scheme to allocate subcarrier, rate, and power for multiuser orthogonal frequency-division multiple-access systems is proposed. The problem is to maximize the overall system rate, under each user's maximal power and minimal rate constraints, while considering the fairness among users. The approach considers a new fairness criterion, which is a generalized proportional fairness based on Nash bargaining solutions and coalitions. First, a two-user algorithm is developed to bargain subcarrier usage between two users. Then a multiuser bargaining algorithm is developed based on optimal coalition pairs among users. The simulation results show that the proposed algorithms not only provide fair resource allocation among users, but also have a comparable overall system rate with the scheme maximizing the total rate without considering fairness. They also have much higher rates than that of the scheme with max-min fairness. Moreover, the proposed iterative fast implementation has the complexity for each iteration of only O(K/sup 2/Nlog/sub 2/N+K/sup 4/), where N is the number of subcarriers and K is the number of users.
Resistance of orthogonal Gaussian fingerprints to collusion attacks

Digital fingerprinting is a means to offer protection to digital data by which fingerprints embedded in the multimedia are capable of identifying unauthorized use of digital content. A powerful attack that can be employed to reduce this tracing capability is collusion. We study the collusion resistance of a fingerprinting system employing Gaussian distributed fingerprints and orthogonal modulation. We propose a likelihood-based approach to estimate the number of colluders, and introduce the thresholding detector for colluder identification. We first analyze the collusion resistance of a system to the average attack by considering the probability of a false negative and the probability of a false positive when identifying colluders. Lower and upper bounds for the maximum number of colluders are derived. We then show that the detectors are robust to different attacks. We further study different sets of performance criteria.
Dynamic real time distortion management over multimedia downlink CDMA

In CDMA networks it is possible to increase the maximum number of users of real time applications by smoothly increasing each end-to-end distortion. We study a protocol for downlink CDMA that allocates source coding rate, channel coding rate and transmitted power based on each user's channel state. The allocation of resources is performed with the constraints of users' maximum distortion and base station's maximum transmitted power in such a way that users operate under carefully controlled distortions. The optimization goal is to minimize the overall distortion in downlink single-cell systems. When the system is highly loaded, the idea for the proposed solution is to initialize the resource allocation with that corresponding to maximum distortion for all users, and then incrementally allocate the remaining transmitted power quota to the user that, at each step, can reduce the distortion with the least use of power. We show that this user must have a high distortion, experience a good channel condition, or generate small interference. We also extend our study to include speech's talk spurt-silence nature and to consider a dynamic model with different offered loads. From simulation results, the proposed algorithm fundamentally reduces the distortions and the necessary maximum transmitted power when the number of users is large, compared with a traditional CDMA scheme (with no distortion control).
URV ESPRIT for tracking time-varying signals

ESPRIT is an algorithm for determining the fixed directions of arrival of a set of narrowband signals at an array of sensors. Unfortunately, its computational burden makes it unsuitable for real time processing of signals with time-varying directions of arrival. The authors develop a new implementation of ESPRIT that has potential for real time processing. It is based on a rank-revealing URV decomposition, rather than the eigendecomposition or singular value decomposition used in previous ESPRIT algorithms. The authors demonstrate its performance on simulated data representing both constant and time-varying signals. They find that the URV-based ESPRIT algorithm is effective for estimating time-varying directions-of-arrival at considerable computational savings over the SVD-based algorithm.
Adaptive QoS for mobile multimedia services over wireless networks

A wireless network that provides multimedia services should control the quality of the transmission link for users, allocating resources according to the service type of each user, be it voice, video, image or data. The paper proposes a scheme to allocate the desired signal to interference noise ratio (SINR) for those users with multimedia services by exploiting power control and smart antennas, while not affecting users with voice service. A method to quickly initialize new users into such a network is also proposed.
Indoor GPS with centimeter accuracy using WiFi

Indoor positioning systems are drawing ascending attention from the academia as well as industry, motivated by a wide variety of indoor location-based services. Among the existing indoor positioning systems, WiFi-based schemes are more favorable because of the ubiquitous WiFi infrastructures. However, both strong non-line-of-sight scenario and severe ambiguity among location-specific fingerprints prevent most existing WiFi-based indoor positioning systems from achieving centimeter accuracy for localization. In this paper, we propose an indoor positioning system which achieves centimeter accuracy and maintains the performance under non-line-of-sight scenarios using a single pair of off-the-shelf WiFi devices. By harvesting the inherent diversity in WiFi systems, the proposed indoor positioning system formulates a large effective bandwidth to provide a fine-grained location-specific fingerprint with a much higher resolution compared with the fingerprints in most WiFi-based indoor positioning systems. Extensive experiment results in a typical indoor environment show that the centimeter accuracy as well as robustness against environment dynamics can be achieved simultaneously with a large effective bandwidth.
Non-Cooperative Feedback Control Game for Secondary Transmitter in Cognitive Radio Network

In this letter, we propose a non-cooperative feedback control game for secondary transmitter, where we assume that secondary users are rational and thus selfish, i.e., they will choose the feedback rate to maximize their own utility functions. The utility function of each user is defined as the downlink data-rate minus a linear price function of the channel state information (CSI) feedback rate. The existence of the Nash-equilibrium of the proposed game is analyzed. Also, in order to satisfy the primary user's interference threshold, power control algorithms are proposed. Simulation results show that the sum-rate of the proposed game is better than that of the equally distributed feedback-size scheme.
Joint source coding diversity and cooperative diversity for multimedia communications

Source coding diversity produces multiple independent source descriptions so as to improve the received quality. Cooperative diversity exploits the broadcast nature of wireless networks by allowing multiple users to relay information for each other, so as to create multiple signal paths. This paper studies what is the best strategy in combining these two types of diversity for real-time multimedia communications by comparing the distortion-rate performance of different joint diversity schemes. Results show that exploring both types of diversity improves the system performance most, but in most of cases, only one type of diversity can achieve most of the diversity gain. The best performance is obtained when the mobile can switch between cooperative and non-cooperative operation depending on the channel conditions. Thus, we remark that it is important to design efficient protocols that manage this switch and provides motivation for all users to cooperate.
Wavelet-based image compression anti-forensics

Because digital images can be modified with relative ease, considerable effort has been spent developing image forensic algorithms capable of tracing an image's processing history. In contrast to this, relatively little consideration has been given to anti-forensic operations designed to mislead forensic techniques. In this paper, we propose an anti-forensic technique capable of removing artifacts indicative of wavelet-based image compression from an image. Our technique operates by adding anti-forensic dither to a previously compressed image's wavelet coefficients so that the anti-forensically modified wavelet coefficient distribution matches a model of the coefficient distribution before compression. Simulation results show that our algorithm is capable of fooling current forensic image compression detection algorithms 100% of the time.
Hidden Chinese Restaurant Game: Grand Information Extraction for Stochastic Network Learning

Agents in networks often encounter circumstances requiring them to make decisions. Nevertheless, the effectiveness of the decisions may be uncertain due to the unknown system state and the uncontrollable externality. The uncertainty can be eliminated through learning from information sources, such as user-generated contents or revealed actions. Nevertheless, the user-generated contents could be untrustworthy since other agents may maliciously create misleading contents for their selfish interests. The passively revealed actions are potentially more trustworthy and also easier to be gathered through simple observations. In this paper, we propose a new stochastic game-theoretic framework, Hidden Chinese Restaurant Game (H-CRG), to utilize the passively revealed actions in stochastic social learning process. We propose grand information extraction, a novel Bayesian belief extraction process, to extract the belief on the hidden information directly from the observed actions. We utilize the coupling relation between belief and policy to transform the original continuous belief-state Markov decision process (MDP) into a discrete-state MDP. The optimal policy is then analyzed in both centralized and game-theoretic approaches. We demonstrate how the proposed H-CRG can be applied to the channel access problem in cognitive radio networks. We then conduct data-driven simulations using the CRAWDAD Dartmouth campus wireless local area network (WLAN) trace. The simulation results show that the equilibrium strategy derived in H-CRG provides higher expected utilities for new users and maintains a reasonable high social welfare comparing with other candidate strategies.
Location-Aware Cooperative Communications Utilizing Linear Network Coding

Cooperative communication can be used to reduce the transmit power of distant mobile units, compared to conventional direct transmission, given the same quality-of-service. However, imposing the constraint of having orthogonal transmission for the source and relays leads to large delay in TDMA systems. For a network of N mobile units, the transmission delay would be N(N+1)/2. In this work, we propose a location-aware cooperation-based scheme that aims to reduce transmit power of distant mobile units while maintaining a low transmission delay. The scheme utilizes a linear network coding protocol, where each mobile unit applies linear network coding to a set of transmit symbols that it has received previously. At the base station, multiuser detection is used to decouple the transmit symbols. Both decode-and-forward and amplify-and-forward cooperative protocols are considered. We show that our proposed scheme achieves a comparable bit-error-rate performance with the conventional cooperation-based TDMA scheme while requiring a delay of (2N-1) time slots, a substantial reduction in the transmission delay.
Analysis of interference in cognitive radio networks with unknown primary behavior

One critical issue in dynamic spectrum access of cognitive radio networks is the analysis of interference caused by Secondary Users (SUs). Most of the current works focus on mitigating the aggregated interference effects of SUs at Primary Users (PUs) in the physical layer. However, the interference is also dynamically related to the communication behaviors between PUs and SUs. In this paper, we analyze the interference caused by SUs in the MAC layer by taking into account the dynamic behaviors between PUs and SUs. Based on the ON-OFF primary channel state model, we derive the close-form expressions for the probability of interference caused by SUs and quantify the interference effect in two scenarios: slotted secondary network and non-slotted secondary network. We also discuss how to control SUs' access behavior such that the normal communication of PUs can be guaranteed. Finally, simulation results are shown to verify the effectiveness of our analysis.
OFDM channel estimation based on time-frequency polynomial model of fading multipath channels

The orthogonal frequency division multiplexing (OFDM) scheme is gaining interest in broadband data communications for wireless communications because of its great immunity to fast fading and intersymbol interference. The channel estimation is a crucial aspect in the design of OFDM systems. In this work, we propose a channel estimation algorithm based on polynomial approximation of the channel parameters in both time and frequency domains. The method exploits both the time and frequency correlation of the channel parameters. The estimator is robust and needs a little prior knowledge about the delay and fading properties of the channel. It can even adjust itself to follow the variation of the channel statistics. Our simulation shows it has more than 5 dB improvement over the existing method under practical channel conditions.