nils-herrmann/clean_scirepeval huggingface dataset:
A high-resolution technique for multidimensional NMR spectroscopy

A scheme for estimating frequencies and damping factors of multidimensional nuclear magnetic resonance (NMR) data is presented-multidimensional NMR data can be modeled as the sum of several multidimensional damped sinusoids. The estimated frequencies and damping factors of multidimensional NMR data play important roles in determining protein structures. The authors present a high-resolution subspace method for estimating the parameters of NMR data, Unlike other methods, this algorithm makes full use of the rank-deficiency and Hankel properties of the prediction matrix composed of NMR data. Hence, it can estimate the signal parameters under low signal-to-noise ratio (SNR) by using a few data points. The effectiveness of the new algorithm is confirmed by computer simulations and it is tested by experimental data.
Service level agreement (SLA) based scheduling algorithms for wireless networks

The objective of scheduling algorithms in wireless networks is to provide quality of service (QoS) for mobile users in a shared environment and at the same time utilize the system resources efficiently. We have introduced a notion of income maximization to increase throughput for multimedia wireless systems and to maintain the QoS for each user above an agreed level. We have proposed greedy and dynamic programming approaches to solve the optimization problem. The simulation results reveal that our scheduling algorithms provide high network throughput, support QoS even under heavy network loads, and generate high income for service provider.
Software optimization of H.263 video encoder on Pentium processor with MMX technology

A key enabling technology for the proliferation of multimedia PCs is the availability of fast video codecs, which are the basic building blocks of many new multimedia applications. Since most industrial video coding standards (e.g., MPEG1, MPEG2, H.261, H.263, etc.) only specify the decoder syntax, there is a lot of room for optimization in a practical implementation. When considering a specific hardware platform like the PC, the algorithmic optimization must be considered in tandem with the architecture of the PC. Specifically, an algorithm that is optimal in the sense of number of operations needed may not be the fastest implementation on the PC. This is because special instructions are available which can perform several operations at once under special circumstances. In this work, we describe a fast implementation of the H.263 video encoder for the Pentium processor with MMX technology.
Towards maximum achievable diversity in space, time, and frequency: performance analysis and code design

Multiple input multiple output (MIMO) communication systems with orthogonal frequency division multiplexing (OFDM) modulation have a great potential to play an important role in the design of the next-generation broadband wireless communication systems. In this paper, we address the problem of performance analysis and code design for MIMO-OFDM systems when coding is applied over both spatial, temporal, and frequency domains. First, we provide an analytical framework for the performance analysis of MIMO-OFDM systems assuming arbitrary power delay profiles. Our general framework incorporates the space-time and space-frequency (SF) coding approaches as special cases. We also determine the maximum achievable diversity order, which is found to be the product of the number of transmit and receive antennas, the number of delay paths, and the rank of the temporal correlation matrix. Then, we propose two code design methods that are guaranteed to achieve the maximum diversity order. The first method is a repetition coding approach using full-diversity SF codes, and the second method is a block coding approach that can guarantee both full symbol rate and full diversity. Simulation results are also presented to support the theoretical analysis.
Progressive image transmission over OFDM systems using multiple antennas

A joint source-channel coding (JSCC) scheme for SPIHT (set partitioning in hierarchical tree) coded image transmission over OFDM (orthogonal frequency division multiplexing) systems with spatial diversity is proposed where no feedback channel is available. By using diversity techniques, the fading effects can be dramatically decreased, and we show that subchannels in OFDM systems are indeed flat Rayleigh fading channels and approach Gaussian noisy channels when the diversity gain gets large; as a result, the system performance can be improved. Simulation results are presented with different numbers of antennas and different multi-path delays and Doppler spreads.
Scalable cross-layer rate allocation for image transmission over heterogeneous wireless networks

We present an efficient real-time JPEG 2000 image transmission system over heterogeneous wireless networks. By exploiting the advantages of the UDP-lite protocol and cross-layer optimization, the system can utilize the limited bandwidth more efficiently. Moreover, a scalable cross-layer rate allocation (SCRA) algorithm is proposed to perform the cross-layer optimization. The algorithm can manage a large number of adjustable parameters with low complexity. The simulation results show that by using the proposed method, 1.5 dB gain in PSNR can be obtained for a fixed transmission rate of 0.5 bpp, or 30% transmission time can be saved under an end-to-end distortion constraint of 30 dB in PSNR.
Theoretical analysis for recursive computation of discrete sinusoidal transforms

The theoretical basis for the optimal unified architecture that can efficiently compute the discrete cosine, sine, Hartley, Fourier, lapped orthogonal, and complex lapped transforms for a continuous input data stream is proposed. It is shown that any discrete transform whose basis functions satisfy the fundamental recurrence formula has a second order autoregressive structure in its filter realization. We also demonstrate that dual generation transform pairs share the same autoregressive structure. Moreover, it is optimal in the sense that the number of the multipliers used is minimum and both speed and area are asymptotically optimal.<<ETX>>
Adaptive SINR threshold allocation for joint power control and beamforming over wireless networks

Cochannel interference is one of the main impairments that degrade the performance of a wireless link. Power control and antenna beamforming are two approaches for improving the performance in wireless networks by appropriately controlling the cochannel interference. Joint optimal beamforming and power control scheme has been considered in previous work. It has been shown that the scheme can reduce the total transmitted power and increase the maximum number of allowable users or maximum achievable signal-to-interference-noise-ratio (SINR) in wireless networks. However the previous scheme only works well in a low SINR situation. In a higher SINR situation, the total transmitted power is much higher than necessary. When the required SINR is higher than a specific value, there are no feasible solutions for power allocation. This problem is caused by the assumption that every mobile user has a fixed and the same SINR requirement (threshold). We introduce a novel adaptive SINR threshold allocation scheme with joint power control and antenna beamforming that can overcome the problems and work in real wireless communication conditions. From simulation results, we can see that our scheme reduce 60 percent of overall transmitted power and increase working area of joint power control and beamforming by 4 dB.
Capacity optimization using subspace method over multicell OFDMA networks

Capacity optimization in a multicell OFDMA system where each cell has multiple users is investigated in this work. The objective is to find an assignment of users to the sets of sub-carrier, their transmission rates for the sub-carrier, and power allocation such that the total system capacity is increased, while users meet a minimum total rate requirement and a power constraint. Since the optimal solution involves an exhaustive search or complex nonlinear integer programming, we develop sub-optimal low complexity algorithms. We propose a two-step scheme: First an initial channel and data rate allocations are determined by two initialization algorithms. Then we refine the assigned rates by an iterative algorithm. From simulation results, the proposed algorithms can efficiently allocate resources to increase the overall system capacity and reduce the allocation outages.
Spectrum trading in heterogeneous cognitive radio networks

In this paper, we investigate the optimal spectrum procurement and pricing from the perspective of a cognitive mobile virtual network operator (C-MVNO), which is a second market between the spectrum owner and the secondary users (SUs). The spectrum procurement consists of spectrum leasing and spectrum sensing, where the latter has uncertain outcome. The SUs are assumed to be heterogeneous in their valuations and demands of the spectrum, which is generally the case in reality. Hence, we use differentiated pricing among the heterogeneous SUs to improve the profit of the C-MVNO and allow the C-MVNO to perform necessary admission control. Modeling the spectrum procurement and trading procedure as a five-stage Stackelberg game, we analyze the optimal decisions for the C-MVNO by using backward induction and propose a computationally efficient method to find them. In simulations, a threshold structure of the solution is observed and our proposed scheme outperforms the single pricing based scheme in prior works.
Clustering based space-time network coding

Many-to-one communication is a challenging problem in practice due to channel fading and multi-user interferences. In this work, a new protocol that leverages spatial diversity through space-time network coding is proposed. The N source nodes are first divided into K clusters, each having Q nodes, and the clusters send data successively in a time-division multiple access way. Each node behaves as a decode-and-forward relay to other clusters, and uses linear coding to combine the local symbol and the relayed symbols. To separate the multi-source signals, each node has a unique signature waveform, and linear decorrelator is used at the receivers. Both the exact Symbol Error Rate (SER) and the asymptotic SER at high signal-to-noise ratios of the M-ary phase-shift keying signal are studied then. It is shown that a diversity order of (N − Q + 1) can be achieved with a low transmission delay of K time slots, which is more bandwidth efficient than the existing protocols. Simulation results also justify the performance gains.
Fractal modeling of mammogram and enhancement of microcalcifications

According to the theory of deterministic fractal geometry, images can be modeled by deterministic fractal objects which are attractors of sets of two dimensional affine transformations. In this paper, a fractal modeling approach is developed to analyze and model mammographic breast tissue background. We 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. Our results are compared with those of the partial wavelet reconstruction and morphological operation approaches. The evaluation results demonstrate that the fractal modeling method is an effective way to enhance microcalcifications, and thereby may facilitate the radiologists' diagnosis. It may also be able to improve the detection of microcalcifications in a computer system.
A resource allocation framework with credit system and user autonomy over heterogeneous wireless network

Future wireless networks will support the growing demands of heterogeneous services. Dynamic resource allocation is essential to guarantee quality of service (QoS) and enhance the network performance. We propose a novel resource allocation framework to cope with the time-varying channel conditions, co-channel interferences, and different QoS requirements in various kinds of services. We define a QoS measurement for delay sensitive applications. We introduce a credit system, where users have their autonomy to decide when and how to use their resources, and users can borrow or lend resources from the system. We also develop a simple feedback mechanism to report the system with the users' QoS satisfaction levels and channel conditions. Then the system will adapt its resource allocation strategy according to the users' feedbacks to favor the users with the bad QoS satisfaction levels or the good channels. We develop adaptive algorithms at both the user and system levels. From simulations, the proposed algorithms efficiently allocate the resources to different types of users. The users' delay constraints are satisfied and the links can survive under a long period of bad channels.
Best-effort cooperative communication without dedicated relays

Traditional decode-and-forward cooperation systems consider dedicated relays, while instead we consider wireless transceivers that cooperatively relay signals in addition to primary communication missions. A system that transmits the additional signal using a best-effort transmission policy within the original transmission energy constraint is considered. To maintain the original energy budget we consider the feasibility of reallocating energy from pilot signals toward the relaying service when channel conditions are stationary. Under the best-effort delivery policy, the node is not obligated to devote energy for relaying signals, nor does it provide a guarantee of signal quality to retransmissions. Instead the relay sacrifices energy at its own discretion, prioritizing the primary communication mission. Using the best-effort delivery policy, we derive an optimal power allocation rule that maintains a fixed symbol error rate for the relay's primary transmission, and further demonstrate cooperative communication gains using the proposed delivery method.
Performance analysis of space-time modulation over time-correlated rayleigh fading channels

Abstmct In th i s paper, we address the problem of characterizing the performance of space-time modulat i on in t ime-correlated fading environment. We show that the space-time signals of square size achieving full diversity in quasi-static fading channels can also achieve full diversity i n t ime-correlated fading channels, irrespectively of the t i m e correlation matr ix . We also generalize the notion of the diversity p roduc t to include t h e effect of t empora l correlation.
Nonlinear collusion attacks on independent fingerprints for multimedia

Digital fingerprinting is a technology for tracing the distribution of multimedia content and protecting them from unauthorized redistribution. Collusion attack is a cost effective attack against digital fingerprinting where several copies with the same content but different fingerprints are combined to remove the original fingerprints. In this paper, we investigate average and nonlinear collusion attacks of independent Gaussian fingerprints and study both their effectiveness and the perceptual quality. We also propose the bounded Gaussian fingerprints to improve the perceptual quality of the fingerprinted copies. We further discuss the tradeoff between the robustness against collusion attacks and the perceptual quality of a fingerprinting system.
Optimal Pricing Strategy for Operators in Cognitive Femtocell Networks

Cognitive femtocell has been envisioned as a promising technology for covering indoor environment and assisting heavy-loaded macrocell network. Although lots of technical issues of cognitive femtocell network have been studied, e.g., spectrum sharing, interference mitigation, etc., the economic issues that are very important for practical femtocell deployment have not been well investigated in the literatures. In this paper, we focus on the pricing issues in the cognitive femtocell network and propose a two-tier pricing game theoretic framework with two models: static and dynamic pricing models. In the static pricing model, we derive the closed-form expressions for pricing and demand functions, as well as the Nash equilibrium pricing strategies for both macrocell and femtocell operators. In the dynamic pricing model, we first model the cognitive users' network access behavior as a two-dimensional Markov decision process and propose a modified value iteration algorithm to find the best strategy profiles for cognitive users. Based on the analysis of users' behavior, we further design an iterative gradient descent algorithm to find the Nash equilibrium pricing strategies for both macrocell and femtocell operators. Simulation results verify our theoretic analysis and show that the proposed algorithm in the dynamic pricing model can quickly converge to the Nash equilibrium prices.
A low bit-rate video codec based on two-dimensional mesh motion compensation with adaptive interpolation

Visual communication over low bandwidth channels, such as the telephone line, requires high compression. Motion estimation and compensation techniques play an important role in achieving high compression by reducing the temporal redundancy inherent in video sequences. Current video coding standards employ the block-matching algorithm to perform motion estimation that produces visually annoying blocking artifacts at low bit-rate. In contrast, 2-D mesh motion compensation produces blocking free prediction by generating a smooth full motion field from the set of node motion vectors using spatial interpolation. However, the assumption of a globally smooth motion field is not valid for most natural occurring scenes, where motion discontinuities exist due to moving objects. We propose a post-processing scheme that refines the set of node motion vectors obtained by the 2-D mesh motion estimation. The approach is to selectively break the smoothness constraint of 2-D mesh for patches that contain motion boundaries by changing the interpolation patterns. Experimental results show improvements in both PSNR and perceptual quality over the 2-D mesh motion compensation and a block-based standard video-coding standard H.263.
Dynamic distributed rate control for wireless networks by optimal Cartel maintenance strategy

In this paper, we consider wireless networks with multiple distributed users. The users transmit their data packets to a communication node via some multi-access protocol. Then the node transmits the packets to a remote destination via a lossy wireless link. The cost for each user's transmission is modelled as a function of its transmission rate (TR). If a user's packet is successfully transmitted to the destination, a profit will be obtained. However, this profit is reduced by the transmission contention among users and the lossy wireless channel. Each user controls its TR to maximize its profit in a distributed manner. If no rule is applied for rate control, each user will select a large TR resulting in having a low performance. In order to improve the system performance, we develop a distributed rate control algorithm using an optimal Cartel maintenance strategy. In this strategy, each user prefers to cooperate for rate control, because deviation from cooperation will be punished by other users for a period of time. From the simulation results, we can see that the proposed distributed scheme can enforce the cooperation among users and achieve much better performance than the noncooperative transmission.
Graphical evolutionary game theoretic framework for distributed adaptive filter networks

Most existing distributed adaptive filtering algorithms focus on de- signing different information diffusion rules, regardless of the nature evolutionary characteristic of a distributed network. In this paper, we study the adaptive network from the game theoretic perspective and formulate the distributed adaptive filtering problem as a graphical evolutionary game. For the nodes in the network, the local combination of estimation information from different neighbors is regarded as different strategies selection. We show that this graphical evolutionary game framework is very general and can unify the existing adaptive network algorithms. Based on the framework, as examples, we further propose two error-aware adaptive filtering algorithms. Finally, simulation results are shown to verify the effectiveness of our method.
Cognitive Radio Networks With Heterogeneous Users: How to Procure and Price the Spectrum?

In this paper, we investigate the optimal spectrum procurement and pricing from the perspective of a cognitive mobile virtual network operator (C-MVNO), which is a second market between the spectrum owner and the secondary users (SUs). The spectrum procurement consists of spectrum leasing and spectrum sensing, where the latter has an uncertain outcome. The SUs are assumed to be heterogeneous in their valuations and demands of the spectrum, which is generally the case in reality. Hence, we use differentiated pricing among the heterogeneous SUs to improve the profit of the C-MVNO and allow the C-MVNO to perform necessary admission control. Modeling the spectrum procurement and trading procedure as a five-stage Stackelberg game, we analyze the optimal decisions for the C-MVNO by using backward induction. The optimal decisions of spectrum sensing, spectrum leasing, admission control, and differentiated pricing are derived, and an algorithm is proposed to compute those optimal decisions efficiently. Our theoretical results are also corroborated by numerical experiments, and a threshold structure of the solution is observed.
Differential Cooperative Communications with Space-Time Network Coding

In multinode cooperative communications, simultaneous transmissions from two or more nodes are challenging due to its asynchronous nature. In addition, channel estimation is a costly task due to the amount of training, especially when the number of cooperative users is large. Considering all these practical challenges in multinode cooperative communications, this paper propose a new transmission scheme, namely differential space-time network coding (DSTNC), to overcome the problems of imperfect synchronization and complicated channel estimation. Each user in the network linearly combines the correctly decoded symbols by a network coding vector, which is designed to achieve full diversity without introducing large time delay. The pairwise error probability (PEP) is analyzed and the design criteria of the DSTNC are derived based on the PEP. The proposed DSTNC scheme can be applied to any number of cooperative users. Simulation results are shown to verify the performance of the proposed transmission scheme.
Template matching for image prediction: A game-theoretical approach

This paper presents a game-theoretical approach to provide a framework for optimal template selection in image prediction. Image prediction is an effective tool for coding still images and intra pictures in videos. Template matching algorithms which use neighboring blocks of the prediction target as templates have been widely used for image prediction. The assumption of these approaches is that the template has similar textural structures as the prediction target. Up to now these approaches all use pre-fixed templates for all prediction targets. However, in real images, these fixed templates are very likely to contain textures that are not or are not significant in the prediction targets and these insignificant textures introduce larger prediction residues. In this paper, we propose a coalitional game in which every pixel is treated as a player and tries to seek partners to form a coalition to capture the textural structure. By forming a coalition, every player in the coalition can obtain a gain of improving the ability of capturing the textural structure of coalition while incurring a cost of introducing textural variance within the coalition. Experimental results show that the proposed game-theoretical approach outperforms the conventional pre-fixed template matching prediction up to 2dB coding gain.