nils-herrmann/clean_scirepeval huggingface dataset:
Capacity comparison of single-tone and multitone CDMA systems

Performance of multitone-CDMA communications over multipath fading channels with coherent RAKE reception is studied. A random-delay, two-path, Rayleigh-fading channel model is considered. Bit error probability for maximal ratio combining is derived. Analytical results show that the multiple-access capacity of a multitone-CDMA system, under conditions of equal bandwidth and equal bit transmission rate, is identical to that of a single-tone direct-sequence spread-spectrum system. It indicates that using more subcarriers to increase the processing gain does not achieve a larger capacity.
Quantifying color image distortions based on adaptive spatio-chromatic signal decompositions

We describe a framework for quantifying color image distortion based on an adaptive signal decomposition. Specifically, local blocks of the image error are decomposed using a set of spatio-chromatic basis functions that are adapted to the spatial and color structure of the original image. The adaptive functions are chosen to isolate specific distortions such as luminance, hue, and saturation changes. These adaptive basis functions are used to augment a generic orthonormal basis, and the overall distortion is computed from the weighted sum of the coefficients of the resulting overcomplete decomposition, with smaller weights chosen for the adaptive terms. A set of preliminary experiments show that the proposed distortion measure is consistent with human perception of color images subjected to a variety of different common distortions. The framework may be easily extended to include any form of continuous spatio-chromatic distortion.
A New Cross Layer Approach to QoS-Aware Proportional Fairness Packet Scheduling in the Downlink of OFDM Wireless Systems

OFDM systems are the major cellular platforms for supporting ubiquitous high performance mobile applications. However, there remain a number of research challenges to be tackled. One of the most important challenges is the design of a judicious packet scheduler so as to make efficient use of the spectrum bandwidth. In this paper, we propose a new QoS-aware proportional fairness (QPF) packet scheduling policy for the downlink of multiuser OFDM systems to allocate radio resource among users. Our proposed algorithm is based on a cross layer design in that the scheduler is aware of both channel and queue state information to achieve proportional fairness while improving each user's QoS performance. Simulation results indicate that the proposed QPF algorithm is efficient in terms of average system throughput, packet dropping probability, and packet delay, while maintaining adequate fairness among users.
Robust Multi-Exposure Image Fusion: A Structural Patch Decomposition Approach.

We propose a simple yet effective structural patch decomposition approach for multi-exposure image fusion (MEF) that is robust to ghosting effect. We decompose an image patch into three conceptually independent components: signal strength, signal structure, and mean intensity. Upon fusing these three components separately, we reconstruct a desired patch and place it back into the fused image. This novel patch decomposition approach benefits MEF in many aspects. First, as opposed to most pixel-wise MEF methods, the proposed algorithm does not require post-processing steps to improve visual quality or to reduce spatial artifacts. Second, it handles RGB color channels jointly, and thus produces fused images with more vivid color appearance. Third and most importantly, the direction of the signal structure component in the patch vector space provides ideal information for ghost removal. It allows us to reliably and efficiently reject inconsistent object motions with respect to a chosen reference image without performing computationally expensive motion estimation. We compare the proposed algorithm with 12 MEF methods on 21 static scenes and 12 deghosting schemes on 19 dynamic scenes (with camera and object motion). Extensive experimental results demonstrate that the proposed algorithm not only outperforms previous MEF algorithms on static scenes but also consistently produces high quality fused images with little ghosting artifacts for dynamic scenes. Moreover, it maintains a lower computational cost compared with the state-of-the-art deghosting schemes.
A new fast algorithm for constructing FP_tree

FP-tree is an efficient algorithm for generating frequent item-sets and the present algorithms are all based on FP_tree generally. But the FP_treepsilas generative process needs much time and it needs to scan database twice. In order to improve the efficiency of constructing the FP_tree, a new fast algorithm called Level FP_tree (abbreviate L-FP_tree) was proposed. The algorithm contains two main parts. Firstly, it scans database and generates equivalence classes of each item. Secondly, it deletes the non-frequent items and rewrites the equivalence classes and then constructs the L-FP_tree.
High-speed realization of trivium based on multi-core cryptographic processor

Trivium, based on block cipher and oriented towards hardware, is a synchronous sequence cipher algorithm which not only applies to resource-constrained environment, but also is suitable for the environment requiring encrypting information quickly. Having studied the characteristics of Trivium and considered the features of multi-core cryptographic processor, this paper proposed a high speed mapping framework on multi core. Meanwhile, we also pay attention to how to split Trivium into multi-core, and give the mapping framework of splitting Trivium into three cores. At the same time, after analyzing its disadvantages, this paper proposed a mapping framework of splitting into double cores. According to these different mapping frameworks, their corresponding throughputs are evaluated, and the results show that the mapping frameworks can quickly realize Trivium.
A load balancing algorithm for homogeneous multiprocessor system

A distributed controlled and sender initiated dynamic balancing algorithm is proposed, aimed at solving load imbalance problem in homogeneous multi-processor system. The proposed algorithm makes use of the CPU run queue length to appraise processor load state, the process runtime to select the load which is suitable to be migrated, the relatively self-contained message mechanism to diffuse processor load state and load balancing requirement, it is adaptive to compute-intensive task. Experimental results prove the effectiveness of the proposed algorithm.
MCI cancellation for multicode wideband CDMA systems

Multicode code division multiple access (CDMA) is a new transmission scheme for flexible and high-speed data communications. The basic idea of multicode CDMA, is to assign multiple channelization codes to any given user. If these channel codes are orthogonal, the self-interference among them can be eliminated in an additive white Gaussian noise (AWGN) channel. However, in a multipath environment, these intrauser signals from different delay paths no longer maintain orthogonality and, thus, cause interference, i.e., multicode interference (MCI), to each other. In high-speed data networks, where the number of users is much less than in the voice networks, the MCI may represent a large portion of the total interference and has a great impact on the system performance. A complex spreading multicode wideband CDMA receiver with RAKE structure and multistage MCI cancellation is studied. By pilot aided channel estimation, the MCI associated with the reference user is regenerated and subtracted from the received signal by a cancellation factor of /spl lambda/. A complete and consolidated theoretical analysis is presented to show that the system performance is significantly improved by the MCI cancellation. The optimal cancellation factor in the kth stage is approximated by /spl lambda//sub opt//sup (k)//spl les/1-2P/sub e//sup (k-1)/, where P/sub e//sup (k-1)/ the error probability of the (k-1)th stage. The optimal value of each stage can be chosen from 0.5 to 0.85 for a wide range of signal to noise ratios.
A goal programming method for generating priority weights based on interval-valued intuitionistic preference relations

Interval-valued intuitionistic preference relations are a powerful means to expressing a decision maker's uncertainty and hesitation about its preference over criteria in the process of multi-criteria decision making. In this paper, we define the notion of consistent interval-valued intuitionistic preference relations. Goal programming models are established for generating priority interval weights based on interval-valued intuitionistic preference relations. Two illustrative numerical examples are furnished to demonstrate how to apply the approach.
Optimal regulation on micro energy grid based on time-shifting agricultural load

To achieve its PV local consumption, the traditional agricultural greenhouses in Gansu Province and its energy supply system are intelligently reformed and demonstrated. The PV wisdom agricultural greenhouse micro energy grid based on time-shifting agricultural load is proposed. This paper compares the scheduling of micro-sources before and after agricultural loads transfer and the daily integrated cost. To allocate the heating equipment rationally and economically, an accurate calculation model of thermal load in greenhouse is constructed. In the paper, based on a new learning factor second-order oscillating culture particle swarm optimization algorithm Simulation is suggested. The simulation proves that the scheduling of the time-shifting load can reduce the system daily integrated cost and verify the correctness of the model and algorithm, meanwhile realize the economical and rational utilization of various energy flows.
Designing a Self-Optimization System for Cognitive Wireless Home Networks

In this paper, we describe the design and implementation of an extensible and flexible self-optimization framework for cognitive home networks that employs cognitive wireless networking and agent-based design principles. We provide a “first-principles” derivation of its architecture based on a careful analysis of user requirements on wireless home networks (WHNs), state the related design objectives and constraints, and address those in the context of present-day and emerging radio platforms. We utilize the cognitive resource manager as an architecture of the individual agents. This architecture serves as a “constraint that de-constrains,” i.e., it allows achieving high system flexibility, while providing structural constraints to ensure robustness. We show that the designed system is capable of solving complex utility maximization problems constrained with user, operator, and regulatory policies in the crowded ISM bands. The system successfully operates on an extensible parameter configuration space across multiple protocol layers. For example, the prototype employs diverse optimization algorithms, and can also benefit from radio environment map information on primary transmitters propagated through the integrated policy mechanism. The proposed system delivers both efficient and robust radio resource management, and enables comfortable WHN experimentation by providing extensibility of sensory inputs, actuation parameters, and optimization algorithms.
Some New Support Vector Machine Models under Given Empirical Risk

The problem of designing a SVM with given empirical risk as well as good generalization ability is proposed in this paper. Some new SVM models, by minimizing the confident risk under given empirical risk, are proposed to achieve this aim. The solving methods for these models are also discussed. It is shown that the smoothing technique is more suitable to solve these models. A numerical experiment is carried out to claim that the empirical risks of these models are well controlled. The main advantage of these models is of good interactive. The trade-off between the empirical risk and the confident risk can be controlled more easily than traditional SVM models. These models are especially adaptive to the problems, in which the distributions of two types of sample are unbalance or the costs of two types of errors are unequal.
MC DS/SFH-CDMA systems for overlay systems

We introduce the slow frequency hopping (SFH) technique to the multicarrier (MC) code-division multiple-access (CDMA) systems for overlay situations. Using lower chip rate, which results in a narrower spectrum for each carrier and hopping the signal in frequency, the MC direct sequence (DS)/SFH system achieves better performance than the MC DS CDMA system in most cases, especially when the bandwidth of the narrowband interference (NBI) is narrower than one subchannel. It also exhibits a stable performance against the variations of the NBI location and bandwidth. When there is no NBI, the two systems perform approximately the same. The evaluation is performed over a frequency selective Rayleigh fading channel, with both Gaussian approximation and Monte Carlo simulation.
Perceptual evaluation of image denoising algorithms

Image denoising has been an active research topic in the past decades for its broad real-world applications, but surprisingly little work has been dedicated to the quality assessment of de-noised images. In this work, we first build a database that contains noisy images at different noise levels and denoised images created by both classical and state-of-the-art denoising algorithms. We then carry out a subjective experiment using a multi-stimulus ranking approach to evaluate and compare the quality of the denoised images. Data analysis shows that there are both considerable agreement and significant variations between human subjects on their opinions of denoised images. Our results also show that state-of-the-art objective image quality models only moderately correlate with subjective opinions, and further investigations that involve both structural fidelity and naturalness measures are desirable in future development of advanced objective models.
A Classification-Based Visual Odometry Approach

Monocular visual odometry is a well-studied, but still unresolved problem. In recent years. Most of the successful monocular visual odometry systems are feature based approaches. However, not all feature points are beneficial for the matching step. As these feature points are extracted for repeatability and rotation invariance, they are not necessarily suitable for the matching process. In this paper, we propose a novel approach for monocular visual odometry, in which we choose the ORB as our feature to meet the real-time requirement, and introduce a random fern classification step before the matching step. The random fern classifier could help us discard those unmatchable feature points, thus speeding up the matching step and enable us to obtain a better visual odometry performance. Experiments on a dataset containing around 2400 images confirm the validity of our proposed approach.
Quality-aware video based on robust embedding of intra- and inter-frame reduced-reference features

With the rapid development of network visual communications, there is an urgent need of effective and efficient video quality assessment (VQA) methods for quality control and resource allocation purposes. In this paper, a spatial and temporal reduced-reference (RR) VQA measure is combined with a robust video watermarking approach, leading to a quality-aware video (QAV) system. At the sender side, both intra- and inter-frame RR features are calculated from the original video based on statistical models of natural video. This is followed by error control coding to improve robustness. The encoded features are then embedded invisibly into the same video signal using a robust angle quantization index modulation based watermarking method in 3D discrete cosine transform domain. At the receiver side, the RR features are extracted and decoded from the distorted video and employed to predict the perceptual degradation of the video signal. Experimental results demonstrate the applicability of the proposed approach to a wide range of distortion types and levels.
Rate scalable video coding using a foveation-based human visual system model

Recently, there have been two interesting trends in image and video coding research. One is to use human visual system (HVS) models to improve the current state-of-the-art coding algorithms by better exploiting the properties of the intended receiver. The other is to design rate-scalable video codecs, which allow the extraction of coded visual information at continuously varying bit rates from a single compressed bitstream. We follow these two trends and propose a foveation scalable video coding (FSVC) algorithm, which supplies good quality-compression performance as well as effective rate scalability to support simple and precise bit rate control. A foveation-based HVS model plays a key role in the algorithm. The algorithm is amenable to the inclusion of various HVS models and adaptable to different video communication applications.
Asymptotic Mutual Information Statistics of MIMO Channels and CLT of Sample Covariance Matrices

In this paper, we consider the fluctuation of mutual information statistics of a multiple input multiple output channel communication systems without assuming that the entries of the channel matrix have zero pseudovariance. To this end, we also establish a central limit theorem of the linear spectral statistics for sample covariance matrices under general moment conditions by removing the restrictions imposed on the second moment and fourth moment on the matrix entries in Bai and Silverstein (2004).
Soft MCI cancellation for turbo-coded OFCDM systems

In this paper, the performance of turbo-coded orthogonal frequency and code division multiplexing (OFCDM) systems is investigated with soft multi-code interference (MCI) cancellation. To regenerate the soft interference signal, the conventional turbo decoding algorithm must be modified to provide log likelihood ratio (LLR) values for all coded bits. Based on the LLR outputs of turbo decoder, two soft decision functions are proposed, called LLR-soft-decision and Gaussian-soft-decision functions. By means of computer simulations, the performance of soft MCI cancellation is studied extensively and compared to that of hard ones. It is shown that in a highly frequency selective channel, the proposed soft decision functions outperform the hard decision function with various channel conditions and system parameters. Furthermore, the Gaussian-soft-decision function provides better performance than the LLR-soft-decision function
A Flexible and Robust Threshold Selection Method

Despite the great prosperity and fast development of image segmentation technology, threshold selection method is still the best choice in many practical applications. The state-of-the-art threshold selection methods perform poorly in segmenting many images with different modalities, such as the magnetic resonance images, cell images, and laser line images. Thus, it is desirable to come up with a more robust method that could segment images with different modalities with the optimum accuracy. To this end, the method should be flexible and its parameters should be adjustable for different types of images. In this paper, we propose to compute the threshold based on the slope difference distribution, which is computed from the image histogram with adjustable parameters. First, the pixels are clustered based on the peaks of the slope difference distribution into different pixel classes. Second, the threshold is selected based on the valleys of the slope difference distribution to separate the pixel classes. The robustness of this threshold selection method relies on the adjustable parameters that could be calibrated to achieve the optimum segmentation accuracy for each specific type of images. The proposed threshold selection method is tested on both the synthesized images and the real images. Experimental results show that the proposed method outperforms the state-of-the-art methods as a whole.
Image processing algorithm for extracting the phase map from structured lights

In structured light three-dimensional measurement systems, the position of stripe in the structure light image reflects the three-dimensional shape information of the object being scanned. In this paper, we propose a semi-automatic 3D image processing algorithm that extracts the phase map from the distorted line pattern. The proposed method uses the stripe extraction approach to segment and cluster the stripe projected over the inspected object after Gaussian convolution denoising. Then a noise-aware smoothing spline fitting process is applied to the result to obtain single continuous stripe and the coordinates of the clustered stripes are calculated to form a phase matrix through a cubic polynomial interpolation procedure. After the camera and projector calibration, the 3D coordinates of the object are computed accurately and robustly. The experimental results show that the work is particularly effective.
Time Series Forecasting Based on Novel Support Vector Machine Using Artificial Fish Swarm Algorithm

Time series analysis is an important and complex problem in machine learning. Support vector machine (SVM) has recently emerged as a powerful technique for solving problems in regression, but its performance mainly depends on the parameters selection of it. Parameters selection for SVM is very complex in nature and quite hard to solve by conventional optimization techniques, which constrains its application to some degree. In this paper, artificial fish swarm algorithm (AFSA) is proposed to choose the parameters of least squares support vector machine (LS-SVM) automatically in time series prediction. This method has been applied in a real Electricity Load Forecasting, the results show that the proposed approach has a better generalization performance and is also more accurate and effective than LS-SVM based on particle swarm optimization.
On Game Theoretic Rate-Maximizing Packet Scheduling in Non-Cooperative Wireless Networks

In many practical scenarios, wireless devices are autonomous and thus, may exhibit non-cooperative behaviors due to self interests. For instance, a wireless user may report bogus channel information in order to gain resource allocation advantages. In this paper, we analyzed the impact of these rationally selfish and non-cooperative behaviors on the performance of packet scheduling algorithms in time-slotted wireless networks. Using a mixed strategy game theoretic model, we found that the traditional rate maximizing packet scheduling algorithms can lead non-cooperative devices to undesirable Nash equilibria, in which the wireless channel is used inefficiently. Motivated by this observation, we proposed a novel game theoretic scheduling approach that can lead to more efficient equilibria where all competing devices can achieve higher rates.
High-speed realization of parallel algorithm for hash computation on multicore cryptographic processor

Hashing algorithms are used widely in information security area. Having studied the characteristics of traditional cryptographic hashing function and considered the features of multi-core cryptographic processor, this paper proposes a parallel algorithm for hash computation well-suited to multicore cryptographic processor. The algorithm breaks the chain dependencies of the standard hash function by implementing recursive hash to get faster hash implementation. We discuss the theoretical foundation for our mapping framework including security measure and performance measure. The experiments are performed on a PC with a PCIE card including multi-core cryptographic processor as the cipher processing engine. The results show a performance gain by an approximate factor of 7.8 when running on the 8-core cryptographic processor.