nils-herrmann/clean_scirepeval huggingface dataset:
Low-Complexity OSIC Equalization for OFDM-Based Vehicular Communications

Vehicular communication systems are usually equipped with orthogonal frequency division multiplexing (OFDM) transceivers that operate on rapidly changing radio propagation environments, which results in high Doppler and delay spreads. More specifically, in these environments, the experienced channels are doubly selective and introduce severe intercarrier interference (ICI) at the receiver. An effective ICI mitigation technique is desired as a constituent part of an ordered successive interference cancellation (OSIC) architecture, which turns out to be computationally efficient, since it may require the solution of linear systems with multiple right-hand sides. To decrease the complexity, several techniques suggest mitigating the ICI by considering only a small number of adjacent subcarriers. However, this approximation introduces an error floor, which may result in unacceptable bit error rates (BER) at high signal-to-noise ratio regimes. In this paper, we propose a new OSIC equalization technique based on an iterative Galerkin projection-based algorithm that reduces the computational cost without sacrificing the performance gains of the OSIC architecture. Furthermore, we suggest a new serial/parallel cancellation architecture that extends the OSIC and has the potential to completely cancel the experienced ICI introduced in high-mobility scenarios. Extensive Monte Carlo experiments have been carried out to validate the accuracy of our framework, revealing intriguing tradeoffs between achieved BER and complexity, and highlighting the importance of designing low-complexity OSIC schemes for OFDM systems operating over double selective channels.
A block quasi-Newton algorithm implemented in the frequency domain

A new block adaptive filtering algorithm is derived. The algorithm belongs to the quasi-Newton class of algorithms which are known to have superior convergence properties as compared to gradient algorithms. An important feature of the algorithm is that it lends itself to efficient implementation in the frequency domain. The computational complexity of the frequency domain version of the algorithm is comparable to that of the self-orthogonalizing frequency domain block LMS algorithm while it converges at a faster rate. Depending on the input signal this improvement in performance may be significant in some practical cases.
A computer-aided system for the detection of prostate cancer based on magnetic resonance image analysis

This paper presents an overview of a computer- aided system for the detection of carcinomas in the prostate gland. The proposed system incorporates information from two different types of magnetic resonance images (MRIs), namely the T2-weighted morphological images and the T1-weighted dynamic contrast enhanced (DCE) images, to extract discriminative features that will be used in the training phase of a classification algorithm for the differentiation between malignant and benign tissue. The resulting feature vectors are also used for the assessment of new patient cases. The pattern recognition scheme is based on probabilistic neural networks (PNNs). The parameters of the PNNs are estimated using the expectation- maximization (EM) algorithm. The performance of the proposed computer-aided detection system is evaluated through training and testing on several patient cases, whose condition has been previously assessed through ultrasound-guided biopsy and MRI examination.
SINR analysis of cognitive underlay systems with multiple primary transceivers in Nakagami-m fading

In this paper the outage probability of a cognitive decode-and-forward relay network operating over Nakagami-m fading channels is evaluated. Based on the underlay approach, secondary transmissions are allowed only in cases where interference constraints on the primary destination receivers are satisfied. Additionally, by taking into consideration the interfering effects coming from the multiple primary transmitters, the signal-to-interference-plus-noise-ratio statistic of the secondary nodes is investigated. The derived results include exact expressions as well as approximated ones for high values of the maximum allowed transmitted power at the cognitive network and/or the interference limited case. We present numerical performance evaluation results for various channel conditions and communication scenarios. These results are complemented by equivalent computer simulated ones, which validate the accuracy of the proposed analysis.
Performance analysis of maximal ratio combining over shadowed-Rice fading channels

An accurate model for analyzing the performance of wireless land mobile satellite (LMS) communication systems is the shadowed-Rice (SR) fading model. In this paper, new simple expressions for the probability density function (PDF) and the cumulative distribution function (CDF) of the sum of possibly correlated non identically distributed squared SR random variables are derived. To this end, a novel approach is introduced for Laplace inverting the moment generating function (MGF) of the sum. Based on this approach the PDF is expressed in a simple infinite chi-squared series form, from which the CDF is also easily obtained. The derived statistics are used to analyze the performance of maximal ratio combining (MRC) over SR fading channels and novel closed-form expressions for various performance criteria such as the outage probability, the ergodic capacity and the bit error probability (BEP) are developed. The results of extensive Monte Carlo simulations are presented, which corroborate our theoretical analysis.
Compressed Sensing Techniques for Decision Feedback Equalization of Sparse Wireless Channels

In this paper new efficient decision feedback equalization (DFE) schemes for channels with long and sparse impulse responses are proposed. It has been shown that under reasonable assumptions concerning the channel impulse response (CIR) coefficients, the feedforward (FF) and feedback (FB) filters may be also approximated by sparse filters. Either the sparsity of the CIR, or the sparsity of the DFE filters may be exploited to derive efficient implementations of the DFE. To this end, compressed sampling (CS) approaches, already successful in system identification settings, can significantly improve the performance of the non sparsity aware DFE. Building on basis pursuit and matching pursuit techniques new DFE schemes are proposed that exhibit considerable computational savings, increased performance properties and short training sequence requirements. To investigate the performance of the proposed schemes the restricted isometry property in the common DFE setup is also investigated.
Adaptive Windowing for ICI Mitigation in Vehicular Communications

The performance of orthogonal frequency division multiplexing systems in vehicular environments suffers from intercarrier interference (ICI) and the inherent non stationarity of the channel statistics. Receiver windowing constitutes an effective technique for enhancing the banded structure of the frequency-domain channel matrix, thus improving the effectiveness of a banded equalizer for ICI mitigation. However, its optimality has been verified only for stationary channels with perfectly known statistics. In non stationary channels, the second-order statistics have to be tracked and the optimal performance can be achieved at the expense of cubic complexity over the number of the subcarriers. To overcome this limitation, an adaptive windowing technique is proposed that is able to track directly an optimal receiver window in terms of average signal-to-interference noise ratio, requiring only linear complexity. Extensive simulation results verify both the ability of the proposed approach to track the time varying channel statistics and its increased robustness to channel estimation errors that are common in vehicular environments.
Optimal power control in cognitive MIMO systems with limited feedback

In this paper, the problem of optimal power allocation in Cognitive Radio (CR) Multiple Input Multiple Output (MIMO) systems is treated. The focus is on providing limited feedback solutions aiming at maximizing the secondary system rate subject to a constraint on the average interference caused to primary communication. The limited feedback solutions are obtained by reducing the information available at secondary transmitter (STx) for the link between STx and the secondary receiver (SRx) as well as by limiting the level of available information at STx that corresponds to the link between the STx and the primary receiver PRx. Monte Carlo simulation results are given that allow to assess the performance achieved by the proposed algorithms.
Channel estimation techniques for half-duplex cooperative communication systems

In this work we present efficient channel estimation algorithms for wideband detect-and-forward (DF) based relay networks. A generalization of a recently proposed transmission model is utilized and the analysis is completely performed in the frequency domain (FD). It is shown that all channels in the network from any node to the destination/receiver node can be blindly estimated up to a phase ambiguities vector, related to the source to destination channel frequency response. Hence, by utilizing a small number of pilot symbols, phase ambiguities can be effectively resolved. As verified by computer simulations, the proposed methods exhibit high estimation accuracy even for a short training sequence, and outperform direct training-based channel estimation. A performance study of the proposed schemes in high SNR conditions is also presented and verified through computer simulations.
Efficient multipath channel estimation using a semi-blind parametric technique

A parametric method for estimating the unknown channel impulse response (CIR) in a semi-blind manner is proposed. The main trait of this method is that, instead of seeking the whole CIR sequence, only the unknown time delays and attenuation factors of the physical channel multipath components are estimated. The technique is based on a suitable application of the subchannel response matching (SRM) criterion. The resulting cost function is separable with respect to the two sets of unknown parameters, i.e. time delays and attenuations. Thus, an efficient two step optimization procedure can be applied. The new method offers significant computational savings and a lower mean square estimation error as compared to existing semi-blind channel estimation methods.
An efficient block adaptive decision feedback equalizer implemented in the frequency domain

In this paper, a new block adaptive decision feedback equalizer (DFE) implemented in the frequency domain is derived. The new algorithm is suitable for applications requiring long adaptive equalizers, as is the case in several high-speed wireless communication systems. The inherent "causality" problem appearing in the block adaptive formulation of the DFE equations is overcome by using tentative decisions in place of the unknown ones within each block. These tentative decisions are subsequently improved by using an efficient iterative procedure, which finally converges to the optimum decisions in a few iterations. This procedure is properly initialized by applying a minimization criterion that utilizes all the available information. The whole algorithm, including the iterative procedure, is implemented in the frequency domain and exhibits a considerable reduction in computational complexity, as compared with the conventional DFE, offering, at the same time, a noticeable increase in convergence speed. Additionally, the level of the steady-state MSE, which is achieved by the new algorithm, is practically insensitive to the block length.
A study of the communication needs in micro-grid systems

The demand for increased power delivery, the extensive incorporation of distributed generators, such as the ones utilizing renewable energy sources, and the adoption of modern electronic devices with processing and communication capabilities, are transforming the power grid into a smart grid. In this work, we provide a brief review of smart grids and, particularly, an integral subsystem, the so-called smart micro-grid, as well as the associated communication technologies, operations and challenges. Furthermore, we outline a very useful connection between techniques that are able to extract information about the consumption signals of domestic appliances by utilizing the theory of sparse coding and the compression of this information when its transmission is required to a remote site. This connection can lead to compression methods that are suitable for transmitting device consumption signals, which constitute one of the most frequent type of smart-grid traffic.
Congestion Forecast Model from Integrated GPS/GIS Data Based on Fuzzy Logic and Neural Network

In dynamic traffic management (DTM) congestion situation forecasting is most valuable information. So far, most the congestion forecasting models are developed on point based traffic data. Yet, in developing countries like China, stationary detectors are very limited and can't support the DTM. Thus the paper presented an adaptive on-line congestion forecasting model from Integrated GPS/GIS data based on fuzzy logic and neural network. This model produces estimates for congestion based on the history and on-line GPS/GIS information. The performance of the model shows good results when compared with real data in Guangzhou city. This study would contribute as a basis for applications of the GPS/GIS based DTM.
Trapping and gate leakage currents effects in large signal modeling of microwave GaN HEMTs

This paper presents the investigation on the influence brought by trapping effects and gate leakage current in GaN HEMTs on the large signal performance as well as the DC characteristics. Kink effect is also taken into consideration in our model. The bias dependent threshold voltage Vds-kink has been studied. Results show that the leakage current will have a great effect on the DC performance while the trapping effects are the opposite case. In terms of large signal performance, the effects brought by gate leakage current will be distinct only when the input power is relatively high while the ones brought by substrate trapping is obvious regardless of the value of input power.
Power Management for Fuel-Cell Power System Cold Start

Fuel cells are difficult to cold start. This letter proposes a power management control strategy for cold-starting fuel-cell systems. The operation principles of the system's cold start are analyzed in detail. A battery powers the load through a bidirectional converter at the beginning of the cold-start state. After the fuel cell finishes its self-start, the fuel cell slips in and the battery slips out of the system, and the fuel cell powers the load independently. The appropriate soft-start control strategies for bidirectional and unidirectional converters are proposed in this letter. A 1-kW fuel-cell power system is built in the laboratory, and experimental results are shown to verify the theoretical analysis.
An LTCC laminated combing filter with layer interconnection structure

This paper presents design and realization of a laminated stripline band-pass filter. With the process of Low-temperature co-fired ceramics (LTCC), a Chebyshev bandpass filter with ten-layer dielectric substrate is fabricated. In order to miniaturize the dimension, the five-layer combing line form is under discussion and applied. To make the measurement easier and practical application available, a layer interconnection structure is designed and connected to the filter. EM simulation is employed to estimate the performance of the filter and do optimization. Simulation result and measuring model show that the effective dimension of the filter is 10 mm×2.9 mm×0.94 mm while it works at center frequency 1.1 GHz. The result of S-parameter indicates that the insert loss in band is less than 1.7 dB and return loss is better than 13 dB.
Distributed suboptimal Cooperative Localization for Multiple Underwater Vehicles

In this paper, we present a new approach to the problem of simultaneous distributed localization for Multiple Autonomous Underwater Vehicles(MAUVs). Cooperative Lo-calization(CL) is a crucial cycle for long range navigation of MAUVs. In the Leader-follower cooperative structure, a collective estimator is processed in the form of a extended Kalman filter. Different from the normal decentralized filter that only be decomposed of the collective filter, the distributed suboptimal method proposed in this paper is to re-construct new measurements to the filter, so as to be decoupled from other sub-filters. Additionally, considering the data re-use problem in the proposed method, the improvement is also proposed. Finally, simulation results are tested to verify the advantage of the distributed suboptimal algorithm, and some comparisons are also provided.
A Joint Intrinsic-Extrinsic Prior Model for Retinex

We propose a joint intrinsic-extrinsic prior model to estimate both illumination and reflectance from an observed image. The 2D image formed from 3D object in the scene is affected by the intrinsic properties (shape and texture) and the extrinsic property (illumination). Based on a novel structure-preserving measure called local variation deviation, a joint intrinsic-extrinsic prior model is proposed for better representation. Better than conventional Retinex models, the proposed model can preserve the structure information by shape prior, estimate the reflectance with fine details by texture prior, and capture the luminous source by illumination prior. Experimental results demonstrate the effectiveness of the proposed method on simulated and real data. Compared with the other Retinex algorithms and state-of-the-art algorithms, the proposed model yields better results on both subjective and objective assessments.
The application of negative angle protective pin in reducing lightning shielding failure on overhead transmission line in a mountainous area

Electro-geometric (EGM) model is now the most popular method for the shielding failure flash-over rate (SFFOR) calculation. In this paper, the Lightning protection theory of the negative angle protective pin is studied on basis of this method, and Installing the negative angle protective pin on the hillside or the peak of the 35kV transmission line in order to reduce the ground shielding failure which is the main cause of lightning flash-over of 35kV transmission lines in DeXing Copper Mine in Jiangxi Province. It was suggested that this research must be considered as one of the important factor in the lightening protection of transmission lines.
A spectral hybrid EBA method for integral equations

We develop a novel spectral hybrid extended Born approximation (EBA) to solve integral equations in electromagnetic wave propagation and scattering problems. The method is a hybridization of the (EBA) and the conjugate gradient-fast Fourier transform (CG-FFT) method. To efficiently solve a two-dimensional integral equation from electromagnetic induction measurements, we have first used the fast Fourier transform to speed up the extended Born approximation to achieve a fast approximate method, referred to as the FFT-EBA method. Then we use the FFT-EBA as a partial preconditioner for the CG-FFT method to arrive at the efficient full-wave EBA-CGFFT method.
A novel soft robot with three locomotion modes

Mollusks have a strong ability adapting to environment, many researchers have developed some soft robots mimicking the mollusks movements, but these soft robots usually have only one locomotion mode, which cannot effectively take into account the movement efficiency and the environment adaptability simultaneously. In this paper, we present a novel soft robot with three locomotion modes which are rolling, Omega crawling and vermiculation. By controlling the body deformation and the connection mechanism of the head and the tail separation, we can realize the three locomotion modes and their switching. Firstly, we present the principle of three locomotion modes switching and analyze the feasibility achieving three locomotion modes, then the detailed structure design of the soft robot is delivered. Secondly, the simulation and experiment for the rolling locomotion are fulfilled to verify the feasibility of the soft robot, as the rolling locomotion is most difficult to realize for the three locomotion modes.
Multidimensional Parameters Estimation of Array Signal via Single Signal Subspace

This paper exploits rotation invariance of matrix pencil based on two parallel uniform linear polarization sensitive arrays, and eigenvalues and the corresponding eigenvectors of the matrix pencil are the rotation matrix and time-space-polarization steering vector of the subarray, respectively. The eigenvalues and steering vector can be used to estimate frequency, two-dimensional angles and polarization of incident signal simultaneously without spectral search. This method is of virtual array element and comparatively high resolution. Moreover this method is equal to multi-dimensional ESPRIT that matrix pencils share the same common set of eigenvectors, and two methods exploit the same information such as eigenvalues and eigenvectors of the matrix pencil. The simulation results verify and analyse the performances of two methods.
Identity Federation Broker for Service Cloud

As the wide adoption of in-cloud services (e.g., software-as-a-service), some major identity related issues are brought up. For enterprises, it usually introduces additional cost and risk to manage identities in services. For service providers, typical pairwise identity federation solutions are not scalable to support single sign-on, service composition, etc. among services for large environment like service cloud. This paper proposes an identity federation broker that introduces a trusted third party as a trust broker to simplify the management of identity federation in a user centric manner. With this solution, the cost and risk of federated identity management for both enterprises and service providers could be significantly reduced. A detailed scenario implementation is given to demonstrate the feasibility of the solution. Moreover, the vulnerability analysis shows how the solution can resist the typical security attacks.
Completion of high-rank ultrametric matrices using selective entries

Ultrametric matrices are hierarchically structured matrices that arise naturally in many scenarios, e.g. delay covariance of packets sent from a source to a set of clients in a computer network, interactions between multi-scale communities in a social network, and genome sequence alignment scores in phylogenetic tree reconstruction problems. In this work, we show that it is possible to complete n × n ultrametric matrices using only n log n entries. Since ultrametric matrices are high-rank matrices, our results extend recent work on completion of n×n low-rank matrices that requires n log n randomly sampled entries. In the ultrametric setting, a random sampling of entries does not suffice, and we require selective sampling of entries using feedback obtained from entries observed at a previous stage.