nils-herrmann/clean_scirepeval huggingface dataset:
Statistical-Based Response-Time Analysis of Systems with Execution Dependencies between Tasks

This paper presents a novel statistical-based approach to Worst-Case Response-Time (WCRT) analysis of complex real-time system models. These system models have been tailored to capture intricate execution dependencies between tasks, inspired by real industrial control systems. The proposed WCRT estimation algorithm is based on Extreme Value Theory (EVT) and produces both WCRT estimates together with a probability of being exceeded. By using the tools developed, an evaluation is presented using three different simulation models, and four other methods as reference: Monte Carlo simulation, MABERA, HCRR and traditional Response-Time Analysis (basic RTA). Empirical results demonstrate that the benefit of the proposed approach, in terms of 1) reduced pessimism when compared to basic RTA and 2) validated guarantee of never being less than the actual response time values. The proposed approach also needs much fewer simulations compared to other three simulation-based methods.
Application of Scale Invariant Feature Transform to Image Spam Filter

Inspired by the keyword-based text filter, this paper proposes an image filter which detects the spam image by matching with user-specified image content. In this way, detecting image spam e-mail is converted into image matching process. Stable local feature detection and representation is a fundamental component of image matching algorithms. SIFT has been proven to be the most robust local invariant feature descriptor. In this process, SIFT algorithm is applied. The images are extracted with SIFT features, which are used to carry out the image matching work. Our experiments demonstrate that SIFT has a good performance in spam image recognition.
A Low Complexity Maximum Likelihood Algorithm for Targets DOA Tracking

This work presents a low complexity bootstrapping filter for target direction-of-arrival tracking in array signal processing, and derives a Monte Carlo maximum likelihood method. Similar to bootstrapping, our new algorithm bypasses the resampling step and directly generates the maximum-likelihood estimates. This simpler target tracking method is compared against the original bootstrapping and the traditional extended Kalman filter. The performance comparison shows no performance loss for our simplified method
A 5 Gb/s Link With Matched Source Synchronous and Common-Mode Clocking Techniques

A 5 Gb/s source-synchronous signaling system was developed utilizing a new clock/data skew minimization technique. The method incorporates a transmit clock delay line and integrating receiver yielding an increased tolerance to high frequency transmit source jitter. The system has the potential to support rapid turn-on without the clock buffer latency of conventional source-synchronous systems. A second method to minimize clock distribution delays with embedded clocking via superposition of clock in the common-mode across two differential pairs was also explored. A test device was fabricated in TSMC's 40 nm LP CMOS process and performance measurements indicate substantial margin improvements, even when the matched source-synchronous system is subjected to realistic source SJ and independent PSIJ noise. Comparable performance was also achieved with embedded common-mode clocking with matched peak swings, indicating it as a potential solution for pin-constrained designs.
An Image-Based Approach to Detection of Fake Coins

We propose a new approach to detect fake coins using their images in this paper. A coin image is represented in the dissimilarity space, which is a vector space constructed by comparing the image with a set of prototypes. Each dimension measures the dissimilarity between the image under consideration and a prototype. In order to obtain the dissimilarity between two coin images, the local keypoints on each image are detected and described. Based on the characteristics of the coin, the matched keypoints between the two images can be identified in an efficient manner. A post-processing procedure is further proposed to remove mismatched keypoints. Due to the limited number of fake coins in real life, one-class learning is conducted for fake coin detection, so only genuine coins are needed to train the classifier. Extensive experiments have been carried out to evaluate the proposed approach on different data sets. The impressive results have demonstrated its validity and effectiveness.
Can one hear the shape of a room: The 2-D polygonal case

We consider the problem of estimating room geometry from the acoustic room impulse response (RIR). Existing approaches addressing this problem exploit the knowledge of multiple RIRs. In contrast, we are interested in reconstructing the room geometry from a single RIR — a 1-D function of time. We discuss the uniqueness of the mapping between the geometry of a planar polygonal room and a single RIR. In addition to this theoretical analysis, we also propose an algorithm that performs the “blindfolded” room estimation. Furthermore, the derived results are used to construct an algorithm for localization in a known room using only a single RIR. Verification of the theoretical developments with numerical simulations is given before concluding the paper.
The Research of Web Services Management Based on WSDM

As the Web Services technology is widely used in the enterprise's core business, how to manage these resources is becoming to extremely important problem. In this paper, after analysising the characteristics of resouce management in distributed environment and the main content of WSDM, a novel Web Service management scheme is introduced, and then the main components in the scheme is introduced, as well as the relationship between them. Also we give out the implementation of the main compoents, and design a typical case used to verify the feasibility of the scheme.
Distributed sensing of signals linked by sparse filtering

We consider the task of recovering correlated vectors at a central decoder based on fixed linear measurements obtained by distributed sensors. A general formulation of the problem is proposed, under both a universal and an almost sure reconstruction requirement. We then study a specific correlation model which involves a filter that is sparse in the time domain. While this sparsity assumption does not allow reducing the description cost in the universal case, we show that large gains can be achieved in the almost sure scenario by means of a novel distributed scheme based on annihilating filters. The robustness of the proposed method is also investigated.
Efficient design of ladder-based transconductor-capacitor filters and equalisers

The paper outlines matrix methods for ladder-based transconductor-capacitor filter and equaliser design and a theory for the canonical realisation of ladder-based transconductor-C filter is presented. A filter and equaliser compiler based on the proposed method is described. As an example, a fifth-order elliptic lowpass filter and a sixth-order group delay equaliser have been designed. It is shown that the order of the equaliser can be reduced by application of special techniques for the approximation of the filter transfer function. A comparison of sensitivity between a cascade of biquadratic stages and the ladder-based realisation is given. Lower sensitivities to component tolerance for both filter and equaliser are displayed by the ladder-based design. Test results from the fabricated filter and equaliser are also given.
A Spectral Graph Uncertainty Principle

The spectral theory of graphs provides a bridge between classical signal processing and the nascent field of graph signal processing. In this paper, a spectral graph analogy to Heisenberg's celebrated uncertainty principle is developed. Just as the classical result provides a tradeoff between signal localization in time and frequency, this result provides a fundamental tradeoff between a signal's localization on a graph and in its spectral domain. Using the eigenvectors of the graph Laplacian as a surrogate Fourier basis, quantitative definitions of graph and spectral “spreads” are given, and a complete characterization of the feasibility region of these two quantities is developed. In particular, the lower boundary of the region, referred to as the uncertainty curve, is shown to be achieved by eigenvectors associated with the smallest eigenvalues of an affine family of matrices. The convexity of the uncertainty curve allows it to be found to within ε by a fast approximation algorithm requiring O(ε-1/2) typically sparse eigenvalue evaluations. Closed-form expressions for the uncertainty curves for some special classes of graphs are derived, and an accurate analytical approximation for the expected uncertainty curve of Erd-s-Rényi random graphs is developed. These theoretical results are validated by numerical experiments, which also reveal an intriguing connection between diffusion processes on graphs and the uncertainty bounds.
Spatial super-resolution of a diffusion field by temporal oversampling in sensor networks

We study the spatial-temporal sampling of a linear diffusion field, and show that it is possible to compensate for insufficient spatial sampling densities by oversampling in time. Our work is motivated by the following issue often encountered in sensor network sampling, namely increasing the temporal sampling density is often easier and less expensive than increasing the spatial sampling density of the network. For the case of sampling a diffusion field, we show that, to achieve trade-off between spatial and temporal sampling, the spatial arrangement of the sensors must satisfy certain conditions. We provide in this paper the precise relationships between the achievable reduction of spatial sampling density, the required temporal oversampling rate, the spatial arrangement of the sensors, and the bound for the condition numbers of the resulting sampling and reconstruction procedures.
Power analysis and optimization for high-speed I/O transceivers

This paper analytically optimizes the overall power consumption of the signaling path of a high-speed link utilizing a source-series terminated (SST) transmitter, either a receive continuous-time linear equalizer (CTLE) or transmit pre-emphasis, and operating over a simple single-pole channel. The analysis derives the optimal allocation between transmit swing and receive gain as well as the allocation of power consumption between the two sides. The analysis also shows that the pre-driver overheads inherent in implementing programmable transmit pre-emphasis make the receive CTLE a substantially more power-efficient solution.
Design Techniques for a 66 Gb/s 46 mW 3-Tap Decision Feedback Equalizer in 65 nm CMOS

This paper analyzes and describes design techniques enabling energy-efficient multi-tap decision feedback equalizers operated at or near the speed limits of the technology. We propose a closed-loop architecture utilizing three techniques to achieve this goal, namely a merged latch and summer, reduced latch gain, and a dynamic latch design. A 65 nm CMOS 3-tap implementation of the proposed architecture operates at up to 66 Gb/s while drawing only 46 mW of power from a 1.2 V supply, achieving 0.7 pJ/bit energy efficiency.
Oxidized layer of CdZnTe studied by C-V characteristics

In this paper, the CZT oxidized layers on cadmium zinc telluride (CZT) wafer formed by two-step chemical passivation process (KOH-KCl + NH4F/H2O2) were investigated by using XRD, ED-XRF, SEM. The results show that the oxidized layer obtained by using this process has a very uniform and compact morphology and consists mainly of TeO2, TeO3 and CdTeO3. In particular, the thickness of the oxidized layer on the CZT was obtained by using C-V measurements for the first time. The dependence of the thickness of the oxidized layer on the passivation time was obtained and the main factors influencing the results of the C-V measurements were also discussed.
The finer directional wavelet transform [image processing applications]

Directional information is an important and unique feature of multidimensional signals. As a result of a separable extension from 1D bases, the multidimensional wavelet transform has very limited directionality. Furthermore, different directions are mixed in certain wavelet subbands. In this paper, we propose a new transform that fixes this frequency mixing problem by using a simple "add-on" to the wavelet transform. In the 2D case, it provides one lowpass subband and six directional highpass subbands at each scale. Just like the wavelet transform, the proposed transform is nonredundant, and can be easily extended to higher dimensions. Though nonseparable in essence, the proposed transform has an efficient implementation based on 1D operations only.
Constructing area Voronoi diagram in document images

Voronoi diagram of image elements provides an intuitive and appealing definition of proximity, which has been suggested as an effective tool for the description of relations among the neighboring objects in a digital image. In this paper, a fast implementation algorithm is proposed for generating area Voronoi diagram of connected components in document images. A closed convex polygon is utilized to bound each connected component, and the contour is represented using Freeman chain coding, from which we can compute the corresponding Freeman chain coding of its expanded contour directly, without recourse to the operation on pixels. While the contours iteratively expand outwards, the Voronoi diagram is constructed by the intersections of the expanded contours from different connected components. The experimental results show that our proposed approach significantly improves the speed of constructing area Voronoi diagram.
Distance-based energy-efficient opportunistic forwarding in mobile delay tolerant networks

Mobile relay-assisted forwarding can improve the network capacity, but meanwhile increase the energy consumption. In this paper, we propose two distance-based energy-efficient opportunistic forwarding (DEEOF) schemes in mobile delay tolerant networks (DTNs). The proposed schemes strike a balance between energy consumption and network performance by maximizing the energy efficiency while maintaining a high packet delivery ratio from two different angles. Specifically, in the developed algorithms, we introduce the forwarding equivalent energy-efficiency distance (FEED) to quantify the transmission distances achieving the same energy efficiency at different time instances. The expected energy efficiency can thus be estimated based on the FEED. Furthermore, the distribution of the greatest forwarding energy efficiency in the predicted period is investigated to provide more accurate prediction for the energy efficiency. The forwarding decision in the algorithms is made by comparing the current energy efficiency and the estimated future expectation. The performance improvement of the proposed algorithms is also demonstrated by simulation, especially for systems where the source has very limited battery reserves.
Current-fed three-phase PWM power factor correction converter

This paper describes a new method to select the optimum sinewave PWM patterns of a large-capacity current source-fed active PWM AC/DC power converter and a practical procedure to design its input AC side low-pass filter which is used to suppress higher harmonics flowing into the utility-grid power source. A feasible test is carried out using a 500 kVA active PWM power converter which is designed and operated for the unique considerations mentioned above. It is verified that these new considerations are more effective to minimize higher harmonic current components flowing into the utility-grid AC line. Furthermore, this experimental work provides highly-efficient AC/DC power converter characteristics under unity power factor correction and sinewave line current shaping schemes.
Tokamak vertical stability coil power supply based on modular multilevel converter

Tokamak is a nuclear fusion reactor that involves high-current power supply systems. Vertical stabilization is required to achieve desired plasma current and elongation in the tokamak. Due to the high current and fast current dynamic response requirements, circuit topology of power supply for vertical stabilization coil is important. In this paper, a full-bridge single-phase modular multilevel converter (MMC) is proposed. Circuit design, voltage balancing strategy and circulating current suppression control method are presented. The proposed topology and strategies are verified in simulations with MATLAB/Simulink.
Spectrum sensing interval optimization and power control for energy efficient cognitive radio networks

Energy efficient cognitive radio networks can simultaneously improve spectrum efficiency (SE) and energy efficiency (EE) of wireless communication networks. This paper studies energy efficient spectrum sensing interval, spectrum sensing energy and transmit power allocation scheme of the secondary user (SU) in CR networks. The EE maximization problem subject to the transmit power constraint and average primary user (PU) interference constraint is formulated as a non-convex optimization problem. Based on the quasiconcave property of the optimization problem, the original problem is transformed into an equivalent concave problem and a Dinkelbach's method based iterative algorithm is used to solve the problem. As a special case, the throughput maximization problem is also investigated. Through simulation results, the impact of transmit power constraint and PU interference constraint on the EE and throughput of the SU are analyzed.
Hybrid ARIMA-BPNN model for time series prediction of the Chinese stock market

Stock price prediction is a challenging task owing to the complexity patterns behind time series. Autoregressive integrated moving average (ARIMA) model and back propagation neural network (BPNN) model are popular linear and nonlinear models for time series forecasting respectively. The integration of two models can effectively capture the linear and nonlinear patterns hidden in a time series and improve forecast accuracy. In this paper, a new hybrid ARIMA-BPNN model containing technical indicators is proposed to forecast four individual stocks consisting of both main board market and growth enterprise market in software and information services sector. Experiment results show that the proposed method achieves the better one-step-ahead forecasting accuracies namely 78.79%, 72.73%, 59.09% and 66.67% respectively for each series than those of ARIMA, BPNN, and Khashei and Bijari's hybrid models.
A Statistical Approach to Response-Time Analysis of Complex Embedded Real-Time Systems

This paper presents Rapid RT, a novel statistical approach to Worst-Case Response-Time (WCRT) analysis targeting complex embedded real-time systems. The proposed algorithm combines Extreme Value Theory (EVT) and other statistical methods in order to produce a probabilistic WCRT estimate. This estimate is calculated using response time data from either Monte Carlo simulations of a detailed model of the system, or from response-time measurements of the real system. The method could be considered as a pragmatic approach intended for complex industrial systems with real-time requirements. The target systems contain tasks with many intricate dependencies in their temporal behavior, which violates the assumptions of traditional analytical methods for response time analysis and thereby makes them overly pessimistic. An evaluation is presented using two simulation models, inspired by an industrial robotic control system, and five other methods as reference.
Learning sparse systems at sub-Nyquist rates: A frequency-domain approach

We propose a novel algorithm for sparse system identification in the frequency domain. Key to our result is the observation that the Fourier transform of the sparse impulse response is a simple sum of complex exponentials, whose parameters can be efficiently determined from only a narrow frequency band. From this perspective, we present a sub-Nyquist sampling scheme, and show that the original continuous-time system can be learned by considering an equivalent low-rate discrete system. The impulse response of that discrete system can then be adaptively obtained by a novel frequency-domain LMS filter, which exploits the parametric structure of the model. Numerical experiments confirm the effectiveness of the proposed scheme for sparse system identification tasks.
A Statistical Response-Time Analysis of Real-Time Embedded Systems

Real-time embedded systems are becoming ever more complex. We are reaching the stage where even if static Response-Time Analysis (RTA) was feasible from a cost and technical perspective, the results of such an analysis are overly pessimistic. This makes them less useful to the practitioner. In addition, the temporal validation and verification of such systems in some applications, e.g., aeronautics, requires the probability of obtaining a worst-case response time larger than a given value in order to support dependable system functions. All these facts advocate moving toward statistical RTA, which instead of calculating absolute worst-case timing guarantees, computes a probabilistic worst-case response time estimate. The contribution of this paper is to present and evaluate such a statistical RTA technique which uses a black box view of the systems under analysis, by not requiring estimates of parameters such as worst-case execution times of tasks. Furthermore, our analysis is applicable to real systems that are complex, e.g., from a task dependencies perspective.
Retrieval of Envelope Images Using Graph Matching

A graph matching approach is proposed to retrieve envelope images from a large image database. First, the graph representation of an envelop image is generated based on the image segmentation results, in which each node corresponds to one segmented region. The attributes of nodes and edges in the graph are described by characteristics of the envelope image. Second, a minimum weighted bipartite graph matching method is employed to compute the distance between two graphs. Finally, the whole retrieval system including two principal stages is presented, namely, rough matching and fine matching. The experiments on a database of envelope images captured from real-life mail pieces demonstrate that the proposed method achieves promising results.
A novel soft-switched current-fed converter with resonant snubber and current overlapping soft-commutation scheme

This paper presents a three phase current-fed space vector modulation converter with a single auxiliary active capacitor type resonant DC link snubber and commutation inductor which can operate under zero current switching (ZCS) commutation. The operating principle of this converter is based upon the current overlapping soft-commutation with the resonant snubber soft-commutation. A new space current vector modulated control strategy of this converter is effectively implemented by applying the space vector pattern to minimize the operating number of auxiliary active switches under the resonant snubber soft-commutation.
Cost-Sensitive Transformation for Chinese Address Recognition

This paper proposes a cost-sensitive transformation for improving handwritten address recognition performance by converting a general-purpose handwritten Chinese character recognition engine to a special-purpose one. The class probabilities produced by character recognition engine for predicting a sample to candidate classes are transformed to the expected costs based on Naive Bayes optimal theoretical predictions firstly. And then candidate probabilities are reestimated based on the expected costs. Two general-purpose offline handwritten Chinese character recognition engines, PAIS and HAW, are tested in our experiments by applying them in handwritten Chinese address recognition system. 1822 live handwritten Chinese address images are tested with multiple cost matrices. Experimental results show that cost-sensitive transformation improves the recognition performance of general purpose recognition engines on handwritten Chinese address recognition.