nils-herrmann/clean_scirepeval huggingface dataset:
Multidimensional Filter Banks and Multiscale Geometric Representations

Thanks to the explosive growth of sensing devices and capabilities, multidimensional (MD) signals — such as images, videos, multispectral images, light fields, and biomedical data volumes — have become ubiquitous. Multidimensional filter banks and the associated constructions provide a unified framework and an efficient computational tool in the formation, representation, and processing of these multidimensional data sets. In this survey we aim to provide a systematic development of the theory and constructions of multidimensional filter banks. We thoroughly review several tools that have been shown to be particularly effective in the design and analysis of multidimensional filter banks, including sampling lattices, multidimensional bases and frames, polyphase representations, Grobner bases, mapping methods, frequency domain constructions, ladder structures and lifting schemes. We then focus on the construction of filter banks and signal representations that can capture directional and geometric features, which are unique and key properties of many multidimensional signals. Next,
Text detection in nature scene images using two-stage nontext filtering

We present a text detection method in natural scene images based on two-stage nontext filtering. Firstly, we detect multi-channel maximally stable extremal regions (MSERs) as character candidates. To reduce the amount of repeating components, we merge the MSERs by choosing the most character-like ones when overlap happens. Then nontext components are filtered out by a two-stage labeling procedure, wherein we combine random forests with CRF. Finally, components labeled as text are grouped into words by an edge-cut strategy, and false positives are eliminated by a HOG-based classifier. The experimental results on the ICDAR2013 database show the effectiveness of the proposed method.
A new scheme to generate multiple weights in the opportunistic beamforming systems

A new scheme to generate multiple weights used in opportunistic beamforming (OBF) is proposed to deal with the performance degradation due to the fewer users in the OBF systems. In the proposed scheme, only two mini-slots are employed to create effective channels, while more channel candidates can be obtained via linearly combining the two effective channels, thus increasing the multiuser diversity and the system throughputs. The simulation results verify the effectiveness of the proposed scheme.
Component carrier selection and interference coordination for carrier aggregation system in heterogeneous networks

In this paper, component carrier selection and interference coordination for carrier aggregation system in Heterogeneous Networks are addressed. To deal with the interference between Macro cell and Pico cell, the proposed scheme dynamically selects the component carriers with the cooperation of the other cells for interference avoidance. Since the selection criterion is based on a modified carrier specific PF metric, the scheme will reserve the proper portion of carriers during which the Macro nodes are partially muted allowing the users in the range extension area to be served with lower interference. Additionally, user cell association by the Harmonic Mean rate metric is used to improve the cell-edge performance further. Simulation results compared with the baseline approach demonstrate that the proposed scheme can achieve high cell-average and cell-edge throughput for the Pico cell in the Heterogeneous Networks.
The observer-based technique for requirements validation in embedded real-time systems

Model-based requirements validation is an increasingly attractive approach to discovering hidden flaws in requirements in the early phases of systems development life cycle. The application of using traditional methods such as model checking for the validation purpose is limited by the growing complexity of embedded real-time systems (ERTS). The observer-based technique is a lightweight validation technique, which has shown its potential as a means of validating the correctness of model behaviors. In this paper, the novelty of our contributions is three-fold: 1) we formally define the observer constructs for our formal specification language namely the Timed Abstract State Machine (TASM) language and, 2) we propose the Events Monitoring Logic (EvML) to facilitate the observer specification and, 3) we show how to execute observers to validate the requirements describing the functional behaviors and non-functional properties (such as timing) of ERTS. We also illustrate the applicability of the extended TASM language through an industrial application of a Vehicle Locking-Unlocking system.
Investigation of competitive adsorption between accelerator and suppressor in TSV copper electroplating

In TSV copper electroplating, the goal is to achieve superfilling deposition. In order to reach the bottom-up in TSV copper electroplating, some additives (accelerator suppressor and leveler) are added into the electroplating bath. To know the relationship between additives in the plating solution is very important. In the present work, by means of linear sweep voltammetry (LSV) and cyclic voltammogram (CV), it is found that there is a competition between the accelerator and the suppressor. At the less negative potential, the accelerator shows a strong adsorption property, and the suppressor plays its role in the relatively more negative potential area, which shows a competitive adsorption relationship between the accelerator and the suppressor during the process of bottom-up filling.
Pulse density modulated zero current soft-switching series resonant high frequency inverter for consumer induction-heated roller

This paper presents the voltage source type half bridge lossless auxiliary inductor assisted series resonant high frequency inverter for induction heated fixing roller in copy machines. This high-frequency inverter treated here can completely achieve zero current soft switching (ZCS) commutation for wide power regulation range under its constant frequency pulse density modulation (PDM) scheme. Its transient and steady state operating principle is originally presented for a constant frequency PDM control strategy under a ZCS operation commutation, together with its output effective power regulation characteristics-based on the PDM. The experimental operating performances of this ZCS-PDM high frequency inverter using IGBTs are illustrated as compared with computer simulation ones. Its power losses and actual efficiency are evaluated and discussed on the basis of simulation and experimental results.
Retrieving imaged documents in digital libraries based on word image coding

A great number of documents are scanned and archived in the form of digital images in digital libraries, to make them available and accessible in the Internet. Information retrieval in these imaged documents has become a growing and challenging problem. For this purpose, a word image coding technique is proposed in this paper, and a Web-based system for efficiently retrieving imaged documents from digital libraries is described. Some image preprocessing is first carried out offline to extract word objects from imaged documents stored in the digital library. Then each word object is represented by a string of feature codes. As a result, each document image is represented by a series of feature code strings of its words, which are stored in a feature code file. Upon receiving a user's request, the server converts the query word into feature code string using the same conversion mechanism as is used in producing feature codes for the underlying imaged documents. Searching is then performed among those feature code files generated offline. An inexact string matching technique, with the ability of matching a word portion, is applied to match the query word with the words in the documents, and then the occurrence frequency of the query word in each corresponding document is calculated for relevant ranking. Preliminary experimental results with some imaged documents of students' theses in the digital library of our university show that the proposed approach is efficient and promising for retrieving imaged documents, with potential applications to digital libraries.
Detecting randomwalks hidden in noise: Phase transition on large graphs

We consider the problem of distinguishing between two hypotheses: that a sequence of signals on a large graph consists entirely of noise, or that it contains a realization of a random walk buried in the noise. The problem of computing the error exponent of the optimal detector is simple to formulate, but exhibits deep connections to problems known to be difficult, such as computing Lyapunov exponents of products of random matrices and the free entropy density of statistical mechanical systems. We describe these connections, and define an algorithm that efficiently computes the error exponent of the Neyman-Pearson detector. We also derive a closed-form formula, derived from a statistical mechanics-based approximation, for the error exponent on an arbitrary graph of large size. The derivation of this formula is not entirely rigorous, but it closely matches the empirical results in all our experiments. This formula explains a phase transition phenomenon in the error exponent: below a threshold SNR, the error exponent is nearly constant and near zero, indicating poor performance; above the threshold, there is rapid improvement in performance as the SNR increases. The location of the phase transition depends on the entropy rate of the random walk.
Efficient image reconstruction for gigapixel quantum image sensors

Recent advances in materials, devices and fabrication technologies have motivated a strong momentum in developing solid-state sensors that can detect individual photons in space and time. It has been envisioned that such sensors can eventually achieve very high spatial resolutions (e.g., 109 pixels/chip) as well as high frame rates (e.g., 106 frames/sec). In this paper, we present an efficient algorithm to reconstruct images from the massive binary bit-streams generated by these sensors. Based on the concept of alternating direction method of multipliers (ADMM), we transform the computationally intensive optimization problem into a sequence of subproblems, each of which has efficient implementations in the form of polyphase-domain filtering or pixel-wise nonlinear mappings. Moreover, we reformulate the original maximum likelihood estimation as maximum a posterior estimation by introducing a total variation prior. Numerical results demonstrate the strong performance of the proposed method, which achieves several dB's of improvement in PSNR and requires a shorter runtime as compared to standard gradient-based approaches.
Bits From Photons: Oversampled Image Acquisition Using Binary Poisson Statistics

We study a new image sensor that is reminiscent of a traditional photographic film. Each pixel in the sensor has a binary response, giving only a 1-bit quantized measurement of the local light intensity. To analyze its performance, we formulate the oversampled binary sensing scheme as a parameter estimation problem based on quantized Poisson statistics. We show that, with a single-photon quantization threshold and large oversampling factors, the Cramér-Rao lower bound (CRLB) of the estimation variance approaches that of an ideal unquantized sensor, i.e., as if there were no quantization in the sensor measurements. Furthermore, the CRLB is shown to be asymptotically achievable by the maximum-likelihood estimator (MLE). By showing that the log-likelihood function of our problem is concave, we guarantee the global optimality of iterative algorithms in finding the MLE. Numerical results on both synthetic data and images taken by a prototype sensor verify our theoretical analysis and demonstrate the effectiveness of our image reconstruction algorithm. They also suggest the potential application of the oversampled binary sensing scheme in high dynamic range photography.
Handwritten and machine printed text discrimination using an edge co-occurrence matrix

We employ an edge co-occurrence matrix (ECM) to distinguish handwritten and machine printed text without resorting to line or word information. The ECM is a modified co-occurrence matrix (CM) on edge images. First, the whole image is divided into overlapping range blocks with fixed size. Then, the ECMs are abstracted from these blocks. The ECM only counts the co-occurring edges connected with each other and its up direction part is the part with most distribution. The liner Support Vector Machine (SVM) is used to classify the features. Because of the similarities of neighboring blocks, the Discriminative Random Fields (DRF) is used to further improve the classification accuracy. The experiments on document images taken from HIT and IMA databases show the effectiveness of our proposed method.
A post-processing approach to Chinese address recognition

In recent years, it has become a focus to make use of the address recognition technology to improve the performance of mail sorting machines. The research in the postal address recognition, which extends the context relation from words to sentences with the use of the address information in post-processing, can effectively improve the recognition performance. In this paper, we propose a divide-and-rule method. The address is divided into high level address and low level address. For high level address, the similarity method with HLA database is presented. For low level address, the Syllable-based language model which applies to Pinyin is discussed. Then a new similarity method in multi-mode is introduced. After post-processing, for high level address, the hit rate rises from 87.63% to 95.12% while the accuracy rate declines by 2.67%, and for low level address, the hit rate increases from 58.16% to 91.81% while the accuracy rate decreases by 9.40%.
Text-independent writer recognition using modified texture and microstructure features

In this paper, we propose effective techniques for automatic writer recognition that operate at different analysis levels. At the texture level, we present a novel 2-D wavelet-based method to replace the traditional 2-D Gabor filter. In our analysis at the microstructure level, we use a floating grid around every edge pixel to record orientation and curvature information to characterize writer individuality. The final unique distance between two handwritten samples is computed as the average of the distances between the individual features. The experiments on a database containing 203 writers of address images exhibit that proposed methods achieve promising results.
Using multiple sequence alignment and statistical language model to integrate multiple Chinese address recognition outputs

Different recognizers may result in different mistakes when they are used to recognize a Chinese address. In this paper, we present a method of combining multiple Chinese address recognition outputs to improve Chinese address recognition accuracy. The method first employs multiple sequence alignment to generate a lattice of candidate hypotheses from multiple different recognizer outputs and then applies statistical language model to choose the maximum likelihood candidate sequence. Taking the maximum as the final decision, the performance of our method is superior, compared to the single recognizers and Miyao's method. The experiments on the address images of real envelopes demonstrate that the proposed method increases the character recognition accuracy rate from 95.80% to 98.38%, with 61.30% error reduction. Furthermore, the corrected sorting rate of an automatic mail sorting system increases from 84.11% to 93.72%.
Depth profiling in piezoelectric photoacoustic imaging: theory and experiment

The depth profiling of piezoelectric photoacoustic (PA) imaging is studied experimentally and theoretically. In the experiment, the PA signals and the layered PA images of semiconductor materials and devices are obtained by adjusting the phase shift. In order to explain the experimental results, a one-dimensional multilayered model with discontinuous thermal impedance between the neighboring layers is used, and expressions for the thermal and acoustic fields in the sample and PZT transducer are presented. Numerical calculations in agreement with practical experimental conditions have been carried out.<<ETX>>
Design Techniques for a 60 Gb/s 173 mW Wireline Receiver Frontend in 65 nm CMOS Technology

Design techniques for a complete 60 Gb/s receiver frontend with equalization, output slicing/demultiplexing, and clocking capabilities are described. Current integration combined with a cascode gate-voltage bias gain-control technique enables energy-efficient implementation of CTLE, FFE, and DFE circuits while operating near the speed limits of the technology. Despite following the DFE that has already in principle sliced the data, adaptive error-sampling requires high gain to resolve small residual error signals-this challenge is addressed by the addition of interleaved, offset-canceled deserializing samplers. Clock generation as well as distribution circuits are implemented to complete the receiver frontend. The proposed 65 nm CMOS receiver operates at 60 Gb/s, consuming 173 mW from 1.2 V and 1.0 V supplies.
Notice of RetractionStudy on the prevention and control measures for job burnout of seamen

Job burnout can result in mental and physical exhaustion of the crew of ocean-going work, which influences the enthusiasm of the seamen and is not conducive to the development of shipping industry and maritime team stability. The specific factors are probe into in this paper from four aspects affecting the offshore crew burnout. The survey results are analyzed by AHP. The consequence shows job stress and interpersonal relationships are more significant on the offshore crew burnout effects. Based on the analysis, the prevention and control measures are provided at last, so as to control and solve the burnout problems of ocean crew effectively.
A novel compressive sensing approach to simultaneously acquire color and near-infrared images on a single sensor

Sensors of most digital cameras are made of silicon that is inherently sensitive to both the visible and near-infrared parts of the electromagnetic spectrum. In this paper, we address the problem of color and NIR joint acquisition. We propose a framework for the joint acquisition that uses only a single silicon sensor and a slightly modified version of the Bayer color-filter array that is already mounted in most color cameras. Implementing such a design for an RGB and NIR joint acquisition system requires minor changes to the hardware of commercial color cameras. One of the important differences between this design and the conventional color camera is the post-processing applied to the captured values to reconstruct full resolution images. By using a CFA similar to Bayer, the sensor records a mixture of NIR and one color channel in each pixel. In this case, separating NIR and color channels in different pixels is equivalent to solving an under-determined system of linear equations. To solve this problem, we propose a novel algorithm that uses the tools developed in the field of compressive sensing. Our method results in high-quality RGB and NIR images (the average PSNR of more than 30 dB for the reconstructed images) and shows a promising path towards RGB and NIR cameras.
Enhanced interleaved partitioning PTS for peak-to-average power ratio reduction in OFDM systems

The independence of the candidates generated in the existing interleaved partitioning partial transmit sequences (IP-PTS) is first investigated. The result shows that these candidates are not fully independent, which leads to performance inferior to the adjacent partitioning PTS (AP-PTS) in which all the generated candidates are mutually independent. To improve the performance of the IP-PTS in peak-to-average power ratio reduction, a novel type of phase sequences for IP-PTS is proposed that can be used to produce fully independent candidates so that IP-PTS can achieve similar performance to AP-PTS, while keeping low complexity.
Weighted-cooperative spectrum sensing scheme using trust in cognitive radio networks

Cooperative spectrum sensing is an advanced technology to improve the utilization of scarce radio spectrum resources. However, there may exist some malicious secondary users (SUs) who are unwilling to cooperate, i.e. provide false sensing reports. Such malicious behavior, if unchecked, the sensing performance of cooperative spectrum sensing will be severely degraded. In this paper, we propose a trust weighted cooperative spectrum sensing scheme to identify malicious SUs and mitigate their harmful effect on sensing performance. This scheme provides a reliable approach to evaluate trust values for SUs. Depending on the trust values, this scheme can detect malicious SUs and filter out their false spectrum sensing results in the process of sensing data fusion. Finally, simulation results validate the effectiveness of the proposed scheme.
A 10Gb/s 10mW 2-tap reconfigurable pre-emphasis transmitter in 65nm LP CMOS

A low-power pre-emphasis voltage mode transmitter architecture with output swing control, pre-emphasis coefficient control, and online impedance calibration is proposed and demonstrated. A 65nm LP CMOS implementation of this architecture dissipates only ~10mW from a 1.2V supply when transmitting 10Gb/s 400mV differential peak-to-peak data with 2-tap pre-emphasis, achieving 1pJ/bit energy efficiency.
Cooperative spectrum sensing using discrete goodness of fit testing for multi-antenna cognitive radio system

In diversity-based multiple antenna cognitive radio system, the performance of the existing cooperative spectrum sensing algorithms based on covariance matrix degrades seriously due to the channel correlation. In this paper, we formulate spectrum sensing as a multinomial distribution test problem, and then apply the Discrete Anderson Darling (DAD) test, one of discrete goodness of fit tests, to examining the above problem. The proposed algorithm dubbed DAD sensing is assessed through Monte Carlo simulations. It is shown that the performance of DAD sensing is robust to the antenna correlation and noise uncertainty.
The Study of a Reflected Persistence Data Layer Framework

Java reflection mechanism is an important technology for improving system flexibility and expandability, which makes the software has the ability of self-adaptive, it changes itself with the external environmental change so as to realize the dynamic evolution of the program. The concept and mechanism of reflection are introduced in this paper, and this paper present a Framework for Persistence Data Layer Based on O/R Mapping. This framework implement dynamic loading and storing the information of Domain Object by adopted the basis of reflection mechanism, and the concrete realizing method example is also presented.
A high reliability classifier using decision trees and AdaBoost for recognizing handwritten Bangla numerals

It is rather hard to achieve high recognition reliability using a single set of features and a single classifier for off-line handwritten numeral recognition systems. In this paper, we present a two-stage classifier for recognizing handwritten Bangla numerals. The first stage classifier is a decision tree based on ID3 algorithm, and the second one is a series of decision trees combined by Weight-Restricting-Based AdaBoost algorithm (WRB AdaBoost). Two sets of features are employed in the different stages. The first set is Open and Closed Cavity (OCC) features, and the other is a combination of OCC features and Ending and Crossing Point (ECP) features. Experiments on numeral images obtained from real Bangladesh envelopes show that the proposed recognition method is capable of achieving high recognition reliability.