nils-herrmann/clean_scirepeval huggingface dataset:
Run-Based Approach to Labeling Connected Components in Document Images

A fast algorithm is proposed in this paper to label connected components in binary document images. Runs are extracted from the image row by row. The positional relations among the runs of current rows and the runs of their preceding rows are represented utilizing trees, where each tree corresponds to a connected component. Only one-pass scan is required for the proposed approach to obtain the characteristics of the connected components, such as bounding rectangle, area, number of pixels. It is thus a fast and effective algorithm. Experimental results have shown that the efficiency of the present algorithm is superior to that of the conventional algorithms in terms of computational speed.
A Context-based Information Retrieval Technique for Recovering Use-Case-to-Source-Code Trace Links in Embedded Software Systems

Post-requirements trace ability is the ability to relate requirements (e.g., use cases) forward to corresponding design documents, source code and test cases by establishing trace links. This ability is becoming ever more crucial within embedded systems development, as a critical activity of testing, verification, validation and certification. However, semi-automatically or fully-automatically generating accurate trace links remains an open research challenge, especially for legacy systems. Vector Space Model (VSM), a notably known Information Retrieval (IR) technique aims to remedy this situation. However, VSM's low-accuracy level in practice is a limitation. The contribution of this paper is an improved VSM-based post-requirements trace ability recovery approach using a novel context analysis. Specifically, the analysis method can better utilize context information extracted from use cases to discover relevant source code files. Our approach is evaluated by using three different embedded applications in the domains of industrial automation, automotive and mobile. The evaluation shows that our new approach can achieve better accuracy than VSM, in terms of higher values of three main IR metrics, i.e., recall, precision, and mean average precision, when it handles embedded software applications.
FTA and FMEA of braking system based on relex 2009

Relex studio 2009 is applied in this paper to analyze the reliability problems of automotive braking system. Firstly, the components of braking system are analyzed and the reliability model is established. Secondly, the Fault Tree Analysis (FTA) is done both qualitatively and quantitatively, which helps identify the weakness to avoid accidents occurrence and minimize losses. Thirdly, the failure modes and effects analysis (FMEA) is processed. Significant data are collected including failure rate, the evaluation of failures paths and the Risk Priority Number (RPN) values. Some important conclusions are presented and serve as instructions to the braking system design.
A Novel Detection Algorithm Based on Capon Beamforming for V-BLAST Systems

A common problem with the exiting V-BLAST detection algorithm is that needs perfect channel information at receiver. But perfect channel information is unavailable in practice, so channel error dramatically degrades the performance of the V-BLAST system. In this paper we design a V-BLAST detector that is robust to channel error. From the point of array signal processing, we present a detection method that is the same as MMSE criterion. Based on the presented method, according to the subspace theory, we develop a robust V-BLAST detector. Computer simulations indicate that the performance of our new robust detector is superior.
An implementation of postal numerals segmentation and recognition system for Chinese business letters

A practical postal numeral segmentation and recognition system for Chinese business letters is presented. Line information for the address blocks is gained from the envelope image by projection, then the sub-image of the postal numerals is segmented. The numerals are recognized by three independent classifiers, and their results are combined to form final results. An improved voting principle based on a priori knowledge of each classifier is utilized to combine multiple classifiers in order to achieve perfect performance for high recognition rate and low error rate. The recognition ability of each classifier, acting with the voting principle, is obtained by statistics for a large set of samples. Different thresholds gained by training are employed for different classes, which can yield better results. A parallel structure hardware is designed to implement a real-time image processing system. The performance of the system is shown to be satisfactory, sufficient for the requirements of practical use.
Figure Tree and its Application in Image Segmentation

Tree is a hierarchical presentation which describes the structure of image space based on the collections of regions from an image. And it is constructed through process of multi-scale image segmentation, in which attributes of each node in the tree are calculated and relationships among the nodes retrieved. As a description of an image’ layout, the knowledge about shapes, or other geometrical features of an object, is easy to be held  Tree with such kind of knowledge can be used to guide the image segmentation and locate an aim.
High-Resolution Three-Dimensional Radar Imaging for Rapidly Spinning Targets

A 3-D inverse synthetic aperture radar imaging method for rapidly spinning targets, i.e., a generalized Radon transform (GRT)-CLEAN algorithm, is proposed in this paper. The signal model is first changed into an equivalent high-speed turntable model after the compensation of the translational motion. Second, based on the relationship between the range profile variation of the spinning targets and the scatterers' positions, the GRT is utilized to estimate the scatterers' positions. Finally, combining the GRT with the modified CLEAN approach, the parameters of each scatterer and, thus, the 3-D image of the targets can be obtained. In addition to the development of the GRT-CLEAN algorithm, the estimation and compensation of the linear translational motion error in the imaging process is also considered in this paper. Good images yielded confirm the effectiveness of the GRT-CLEAN algorithm in the simulations.
A Combined Genetic Algorithm and Orthogonal Transformation for Designing Feedforward Neural Networks

In this paper, a hybrid algorithm is proposed for designing feedforward neural networks. A genetic algorithm is used to tune the connections and parameters between the input layer and the hidden layer, and orthogonal transformation is applied to tune the connections and parameters between the hidden layer and the output layer. In this way, both the structure and parameters of a neural network can be optimized efficiently. Simulations are presented to demonstrate the effectiveness of the proposed approach.
Fast blind spectrum sensing method based on determinant of covariance matrix

Spectrum sensing is the key problem for cognitive radio systems. A fast blind sensing method based on Determinant of Covariance Matrix (DCM) of the received signals is proposed to sense the available spectrum for the cognitive users with the help of the multiple antennas at the receiver of the cognitive users. The greatest advantage of the new method is that it requires no information of the noise power and without any eigen-decomposition (or SVD) of sample covariance matrix. Both the simulation and the analytical results demonstrate that the proposed method is effectiveness and robustness.
A multi-GHz area-efficient comparator with dynamic offset cancellation

In this paper we propose a dynamic impedance modulation technique to significantly improve the speed of comparators utilizing dynamic-offset-cancellation (DOC). Measurements from a 65nm test-chip show that DOC comparators utilizing the proposed technique achieve 6X lower input-referred offset and 9X better power supply noise rejection than a traditional StrongArm comparator with only a 20% speed penalty at identical core comparator area (98µm2) while dissipating 455µW.
A study on the clinical classification model for the hand venule of baby pneumonia based on decision tree algorithm

The decision tree algorithm, an analytical and predictive model to describe datum under the influence of different variables, has been successfully put to use in diagnosing and treating certain diseases. To meet the requirements and characteristics of the clinical classification for the hand venule of baby pneumonia, this paper proposes a new classification model for the hand venule of baby pneumonia on the basis of the decision tree algorithm and the differentiation rules in traditional Chinese medicine. The experiments show that the algorithm's classification accuracy rate is over 80% for the clinical measured data and the model also has a good clinical practical value with efficient speed and robust characteristic.
Fast non-local filtering by random sampling: It works, especially for large images

Non-local means (NLM) is a popular denoising scheme. Conceptually simple, the algorithm is computationally intensive for large images. We propose to speed up NLM by using random sampling. Our algorithm picks, uniformly at random, a small number of columns of the weight matrix, and uses these “representatives” to compute an approximate result. It also incorporates an extra column-normalization of the sampled columns, a form of symmetrization that often boosts the denoising performance on real images. Using statistical large deviation theory, we analyze the proposed algorithm and provide guarantees on its performance. We show that the probability of having a large approximation error decays exponentially as the image size increases. Thus, for large images, the random estimates generated by the algorithm are tightly concentrated around their limit values, even if the sampling ratio is small. Numerical results confirm our theoretical analysis: the proposed algorithm reduces the run time of NLM, and thanks to the symmetrization step, actually provides some improvement in peak signal-to-noise ratios.
A Web-based System for Retrieving Document Images from Digital Library

A web-based system for retrieving imaged documents from a digital library is described in this paper. First, some image preprocessing is performed off-line on the underlying imaged document to extract its word objects. Then, each word object is represented by a string known as its feature code, based on which a feature code file of the corresponding document is constructed. On the web interface side, the system allows the user to input a set of query words and indicate either to perform "AND" or "OR" operation on them. Once receiving user's request, the system will process each query word and combine the results based on the "AND" or "OR" operation the user has chosen. As for each query word, it is first looked up in an index table that stores words being queried before. If matches are found, results will be retrieved from the index table directly and stored temporarily for subsequent merging. This speeds up searching and makes the system an incremental intelligence system. Otherwise, the system will convert the query word to a feature code string and employ a partial word matching approach to perform search on the pre-generated feature code files. Preliminary experimental results with the imaged documents of students' theses provided by our digital library show that the proposed system is efficient and promising for document image retrieval, and thus has potential applications to digital libraries.
Word spotting in Chinese document images without layout analysis

An approach to searching user-specified words/phases in Chinese document images, without the requirements of layout analysis, is proposed in this paper. Bounding boxes of Chinese character images are first determined using the connected component analysis. Next, a suitable character from the user-specified word/phrase is chosen as the initial character to search for a matching candidate in the document. Once a matched candidate is found, its adjacent characters in the horizontal and vertical directions are examined for matching with other corresponding characters in the user-specified word/phrase, subject to the constraints of positional relation and size similarity. The character matching is done in two stages. The coarse matching is carried out based on the stroke density features. A weighted Hausdorff distance is proposed for the second matching phase. Experimental results show that the proposed method can effectively search the user-specified Chinese word/phrase from horizontal or vertical text lines of document images.
PAPR reduction for real baseband OFDM signals

When applying the existing partial transmit sequences (PTS) schemes directly to real-valued systems, one may encounter a difficulty in constructing phase sequences sets with conjugate symmetry. To overcome this difficulty, two PTS schemes, the adjacent partition (AP-PTS) and the interleaved partition (IP-PTS), are proposed in this paper. For the AP-PTS, the difficulty is circumvented by rearranging the data sequence, while for the IP-PTS, constructing phase sequences sets is avoided by generating the real-valued OFDM candidate signals in the time domain directly. The performances of the two schemes are evaluated via simulations. The results show that both schemes can effectively reduce the PAPR of a real-valued OFDM system
Nonlinear Optical Properties of Stimulated Brillouin scattering to submerged objects detecting

Nonlinear optical properties of stimulated Brillouin scattering to submerged objects detecting are analyzed. With the characteristics of threshold, SBS only occurs when the incident laser is focused in the focusing regime. When there is an object present in front of the focus leading to lower incident intensity then SBS don't occur. The backward SBS signal depends on the location of the submerged object. With the object come near to focusing regime, the backward Stokes signal rise up from zero to a Maximum, and then grow to saturation. The delay time of echo signal to pump signal can give the location of submerged object. Relationships of reflected energy dependent on location of object in SBS cell is analyzed both theretically and experimentally.
Transformational Specification of Complex Legacy Real-Time Systems via Semantic Anchoring

RTSSim is a framework for simulating models extracted from complex legacy real-time systems which are task-oriented, run on a single processor and are developed in C. Such RTSSim models describe functional and temporal behavior as well as the resource usage of the system. However, the semantics specification of RTSSim models remains a challenging problem indeed, especially with tractable complexity to obtain a formal model which can be analyzed for instance by a model checking tool. In this paper, we present an approach towards using semantic anchoring for the transformational specification of RTSSim models, by relying on units with well-defined operational semantics and tool support. Specifically, Timed Automata with Tasks (TAT) in TIMES is chosen as the semantic unit with the purpose of anchoring different behavioral concerns of RTSSim models in all aspects. In this respect, model transformations are conducted at the meta-model level allowing the original operational semantics of RTSSim models to be preserved, while at the same time it can be presented in TIMES models in terms of a network of TAT.
Distance-Based Energy-Efficient Opportunistic Broadcast Forwarding in Mobile Delay-Tolerant Networks

Mobile-relay-assisted forwarding in delay-tolerant networks (DTNs) can improve network capacity and packet delivery ratio but, at the same time, may significantly increase energy consumption at the system level. In this paper, we propose a distance-based energy-efficient opportunistic forwarding (DEEOF) framework for the broadcast transmission in mobile DTNs. DEEOF strikes a balance between energy consumption and network performance by maximizing the energy efficiency while maintaining a high packet delivery ratio. In the proposed algorithms, we define the metric of the forwarding equivalent energy efficiency distance (FEED) for broadcast transmission to quantify the transmission distances achieving the same energy efficiency at different time instances, or with different numbers of the relays in the source's transmission radius. Based on the concept of FEED, we propose two DEEOF algorithms for opportunistic broadcast forwarding, which makes the forwarding decision by comparing the current energy efficiency with the estimated future expectation and distribution, respectively. The performance improvement of the proposed DEEOF algorithms is also demonstrated by simulation, particularly for the cases in which the source has very limited battery reserves.
Sampling and reconstructing diffusion fields with localized sources

We study the spatiotemporal sampling of a diffusion field generated by K point sources, aiming to fully reconstruct the unknown initial field distribution from the sample measurements. The sampling operator in our problem can be described by a matrix derived from the diffusion model. We analyze the important properties of the sampling matrices, leading to precise bounds on the spatial and temporal sampling densities under which perfect field reconstruction is feasible. Moreover, our analysis indicates that it is possible to compensate linearly for insufficient spatial sampling densities by oversampling in time. Numerical simulations on initial field reconstruction under different spatiotemporal sampling densities confirm our theoretical results.
A Computable Fourier Condition Generating Alias-Free Sampling Lattices

We propose a Fourier analytical condition linking alias-free sampling with the Fourier transform of the indicator function defined on the given frequency support. Our discussions center around how to develop practical computation algorithms based on the proposed analytical condition. We address several issues along this line, including the derivation of simple closed-form expressions for the Fourier transforms of the indicator functions defined on arbitrary polygonal and polyhedral domains; a complete and nonredundant enumeration of all quantized sampling lattices via the Hermite normal forms of integer matrices; and a quantitative analysis of the approximation of the original infinite Fourier condition by using finite computations. Combining these results, we propose a computational testing procedure that can efficiently search for the optimal alias-free sampling lattices for a given polygonal or polyhedral shaped frequency domain. Several examples are presented to show the potential of the proposed algorithm in multidimensional filter bank design, as well as in applications involving the design of efficient sampling patterns for multidimensional band-limited signals.
Optimal energy harvesting-aided spectrum sensing sensors scheduling in cognitive radio networks

A cognitive radio network consisting of multiple energy harvesting spectrum sensors is considered. The goal of this paper is to design an optimal sensors allocation strategy to maximize the sum detected channel availability probability. The sum detected channel availability probability maximization problem is formulated as a non-convex optimization problem. A Greedy algorithm is proposed to solve the non-convex optimization problem. Simulation results show that our proposed method achieves a significantly higher spectrum sensing performance than the random allocation scheme.
Combined Multiple Weight Vectors and Receive Antenna Selection

In order to improve the throughput of the opportunistic beamforming, a technique enhancing opportunistic beamforming is proposed, which combines the multiple vectors and receive antenna selection technique. Using multiple random weighting vectors at the base station and additional receive antennas at the mobile users can be viewed as increasing the effective number of users on the downlink. Significant performance improvement is achieved especially for small number of users. The simplicity and inexpensive deployment of this technique make it a highly desirable enhancement to opportunistic beamforming.
Hardware in the loop based 6DoF test platform for multi-rotor UAV

Unmanned Aerial Vehicles (UAVs) rely on robust control algorithms to accomplish their mission. But, the classical design and testing process with its multiple stages; enormous resources, efforts and a long cycle of time are exhausted. In addition, the process has another serious drawback which is the simulation delay where the simulation period is too long especially for complex embedded systems with multi parameters. In this paper, we present an effective method for designing, evaluating and testing any multi-rotor UAV flight control system, through building up a six-degrees-of-freedom (6DoF) indoor flight environment for real-time simulation, based on MATLAB-Simulink/FPGA Hardware-In-the-Loop simulation (HIL). The platform aims to increase the development process pace and simplify its complexity. Also, the use of FPGA accelerates the simulation to decrease simulation lag. To demonstrate the effectiveness of the proposed testbed, an attitude estimation algorithm based on direction cosine matrix(DCM) is developed and verified on the HIL testbed.
Improved nearest neighbor based approach to accurate document skew estimation

The nearest-neighbor based document skew detection methods do not require the presence of a predominant text area, and are not subject to skew angle limitation. However, the accuracy of these methods is not perfect in general. In this paper, we present an improved nearest-neighbor based approach to perform accurate document skew estimation. Size restriction is introduced to the detection of nearest-neighbor pairs. Then the chains with a largest possible number of nearest-neighbor pairs are selected, and their slopes are computed to give the skew angle of document image. Experimental results on various types of documents containing different linguistic scripts and diverse layouts show that the proposed approach has achieved an improved accuracy for estimating document image skew angle and has an advantage of being language independent.
Design and finite element analysis of a novel longitudinal-torsional hybrid ultrasonic motor

This paper reported an ultrasonic motor using the 1st longitudinal and the 2nd torsional (L-T) modes. The stator slots were useful to tune the resonance frequencies and increased L-T displacements largely. By cutting slots, which determined the number of oscillators, the whole stator was changed into independent vibrators. The more independent vibrators, the bigger power it outputs. The stator possessing different number of vibrators can realize frequency tuning by increasing outer diameter. Piezoelectric elements were pasted on the outer surface of the metal substrate. L-T hybrid vibrations of the driving surface can be excited when suitable electrical signals were applied to piezoelectric units. The working principle of the designed motor was analyzed particularly. The optimal slots dimension and location, including vibration characteristics can be obtained using the finite element analysis software, ANSYS. The analyzed results and experiments verified the proposed motor possessing good performance.