nils-herrmann/clean_scirepeval huggingface dataset:
High contrast ultrasound images of defects in food package seals

Previous work to detect defects in food packaging seals using pulse-echo ultrasound inspired the backscattered amplitude integral (BAI) imaging technique, which could reliably identify channels with diameters 38 /spl mu/m or larger at a center frequency of 17.3 MHz (/spl lambda/=86 /spl mu/m). The current study presents two new processing techniques that more reliably reveal smaller channels (/spl ap/6 /spl mu/m in diameter). The RF sampling technique (RFS) displays a single, time-gated, pressure value from the received (not envelope-detected) RF waveform at each transducer position. The RF correlation technique (RFC) calculates the correlation coefficients of the RF signals with a reference signal that does not pass through a channel. The correlation coefficient can be calculated for the entire RF signal (RFCE) or over a short segment of the RF signal (RFCS). The performance of these imaging methods for detecting channel defects is investigated for plastic and aluminum foil trilaminate films with 6, 10, 15, 38, and 50 /spl mu/m channels filled with water or air. Data are collected with a focused ultrasound transducer (17.3 MHz, 6.35 mm in diameter, f/2, 173 /spl mu/m -6 dB pulse-echo lateral beamwidth at the focus) scanned over a rectangular grid, keeping the package in the focus. Performance is measured using detection rates, image contrast, and contrast-to-noise ratio (CNR). Both RFS and RFCS have improved detection rates relative to BAI for channels 15 /spl mu/m or smaller. The RFCS technique is the most effective at smoothing the background, leading to the greatest CNR improvements.
An approach to blind estimation of OVSF sequence in weak WCDMA signals

For the problem of the WCDMA uplink signals to blindly estimate the Orthogonal Variable Spreading Factor (OVSF) code sequence in low signal to noise ration (SNR), this paper proposed an approach that is developed using correlation matrix accumulation and singular value decomposition. The parameters of the WCDMA signals (such as the period and chip interval of the OVSF sequence) need to be known. The received signals are divided into multiple continuous observation vectors based on the OVSF sequence period with the fixed value as the starting point. Then combining the accumulation and eigenanalysis of correlation matrix, and the OVSF sequence of the WCDMA signals is blindly estimated from the main component eigenvector. Then the WCDMA signals can be despreaded with the OVSF sequence. The simulation experiments show the method can work well in lower SNR environments.
GIFT: Towards Scalable 3D Shape Retrieval

Projective analysis is an important solution in three-dimensional (3D) shape retrieval, since human visual perceptions of 3D shapes rely on various 2D observations from different viewpoints. Although multiple informative and discriminative views are utilized, most projection-based retrieval systems suffer from heavy computational cost, and thus cannot satisfy the basic requirement of scalability for search engines. In the past three years, shape retrieval contest (SHREC) pays much attention to the scalability of 3D shape retrieval algorithms, and organizes several large scale tracks accordingly [1]– . However, the experimental results indicate that conventional algorithms cannot be directly applied to large datasets. In this paper, we present a real-time 3D shape search engine based on the projective images of 3D shapes. The real-time property of our search engine results from the following aspects: 1) efficient projection and view feature extraction using GPU acceleration; 2) the first inverted file, called F-IF, is utilized to speed up the procedure of multiview matching; and 3) the second inverted file, which captures a local distribution of 3D shapes in the feature manifold, is adopted for efficient context-based reranking. As a result, for each query the retrieval task can be finished within one second despite the necessary cost of IO overhead. We name the proposed 3D shape search engine, which combines GPU acceleration and inverted file (t wice), as GIFT. Besides its high efficiency, GIFT also outperforms state-of-the-art methods significantly in retrieval accuracy on various shape benchmarks (ModelNet40 dataset, ModelNet10 dataset, PSB dataset, McGill dataset) and competitions (SHREC14LSGTB, ShapeNet Core55, WM-SHREC07).
Optimal design of Cosine Modulated Filter Banks using quantum-behaved particle swarm optimization algorithm

In the paper, a new method for designing Cosine Modulated Filter Banks (CMFB) is proposed, the prototype filter is designed by optimization method. Firstly, the method appropriately relaxes the limit conditions for perfect reconstruction, the design problem of CMFB is formulated as a nonlinear and unconstrained optimization of an objective function, which stopband cutoff frequency is fixed, passband cutoff frequency is adjusted to minimize the cost function which satisfies reconstruction condition, and directly designs the prototype filter. The simulation results illustrate the proposed method and its improvement over other methods in terms of amplitude distortion, aliasing distortion, signal to noise ratio and reconstruction performance.
Region similarity arrangement for image retrieval

We propose a promising method of geometric verification to improve the precision of Bag-of-Words (BoW) model in image retrieval. Most previous methods focus on the positions of interest points or the absolute differences of regions' scales and angles. In contrast, our method, named Region Similarity Arrangement (RSA), exploits the spatial arrangement of interest regions. For each image, RSA constructs a Region Property Space, regarding each region's (scale, angle) pair as a point in a polar coordinate system, and encodes the arrangement of these points into the BoW vector. From experimental results on Holidays, Oxford5K and Paris, RSA could get comparable results with sate-of-the-art methods. In addition, RSA increases no extra memory and negligible computational consumption compared with the baseline BoW approach.
A Boosting Approach to Exploit Instance Correlations for Multi-Instance Classification

We propose a Boosting approach for multi-instance (MI) classification. Lp-norm is integrated to localize the witness instances and formulate the bag scores from classifier outputs. The contributions are twofold. First, a flexible and concise model for Boosting is proposed by the Lp-norm localization and exponential loss optimization. The scores for bag-level classification are directly fused from the instance feature space without probabilistic assumptions. Second, gradient and Newton descent optimizations are applied to derive the weak learners for Boosting. In particular, the instance correlations are exploited by fitting the weights and Newton updates for the weak learner construction. The final Boosted classifiers are the sums of iteratively chosen weak learners. Experiments demonstrate that the proposed Lp-norm-localized Boosting approach significantly improves the MI classification performance. Compared with the state of the art, the approach achieves the highest MI classification accuracy on 7/10 benchmark data sets.
Multilevel video representation with application to keyframe extraction

Content-based video analysis calls for efficient video representation. In this paper, a novel multi-level representation of video is proposed based on the principle components derived from low-level visual features. It can characterize the video content from the coarse level to the fine level according to its intrinsic structure. This representation form provides a flexible scheme for video content analysis such as summarization, classification, and retrieval. A newly proposed subspace method, kernel based PCA, is explored to achieve this conveniently. The application in keyframe extraction is investigated to demonstrate the benefits of this representation.
Novel Unequal Dividing Power Divider With 50  $\Omega$ Characteristic Impedance Lines

In this letter a novel power divider (PD) structure with uniformed characteristic impedance lines and a fixed isolation resistor equal to source and load impedances has been proposed. Unlike traditional power dividers, the electrical lengths of the uniformed 50 Ω branches are the only parameters that need to be adjusted to achieve any required power division ratios, which makes it a flexible and adaptable layout for most desires. Arbitrary power division ratio, especially large dividing ratio that is difficult to achieve by conventional Wilkinson PD, can be obtained easily by applying the proposed structure. For verification, the measured results of a 2 : 1, a 4 : 1 and a 9 : 1 PDs operating at 3 GHz are shown in this letter.
Exploring Context and Content Links in Social Media: A Latent Space Method

Social media networks contain both content and context-specific information. Most existing methods work with either of the two for the purpose of multimedia mining and retrieval. In reality, both content and context information are rich sources of information for mining, and the full power of mining and processing algorithms can be realized only with the use of a combination of the two. This paper proposes a new algorithm which mines both context and content links in social media networks to discover the underlying latent semantic space. This mapping of the multimedia objects into latent feature vectors enables the use of any off-the-shelf multimedia retrieval algorithms. Compared to the state-of-the-art latent methods in multimedia analysis, this algorithm effectively solves the problem of sparse context links by mining the geometric structure underlying the content links between multimedia objects. Specifically for multimedia annotation, we show that an effective algorithm can be developed to directly construct annotation models by simultaneously leveraging both context and content information based on latent structure between correlated semantic concepts. We conduct experiments on the Flickr data set, which contains user tags linked with images. We illustrate the advantages of our approach over the state-of-the-art multimedia retrieval techniques.
Statistical analysis of split spectrum processing

This work provides a statistical analysis of Split Spectrum Processing (SSP) performance in detecting multiple targets. The investigation is performed under two conditions: (i) known a priori target spectra (i.e., center frequency and bandwidth) which, in turn, identifies the optimal spectral range for processing, and (ii) adaptively obtaining the processing frequencies using group delay moving entropy. The group delay moving entropy (GDME) method was introduced to select the optimal frequency regions for SSP when detecting multiple targets. The effectiveness of this technique is statistically demonstrated in this paper. The performance is measured in terms of Normalized Signal-to-Noise Ratio and probability of target detection. SSP with known target information yields a slightly higher probability of detection compared to SSP using GDME, while both cases achieve comparable SNR enhancement. SSP results were compared to the optimal bandpass filter performance and shown to be superior.
DOA estimation of coherent wideband LFM signals based on fractional fourier transform and virtual array

A method for the direction of arrival (DOA) estimation of coherent wideband linear frequency modulated (LFM) signals is researched based on fractional Fourier transform (FRFT) and virtual array transform. In this algorithm, the observed signals are first transformed to fractional Fourier domain, and then construct correlation signals matrix of the fractional Fourier domain, and the decorrelation ability is obtained by virtual array transform, which can settle the problem of not finding coherent signals in routine MUSIC and ESPRIT algorithm. Compare to the spatial smoothing algorithm, this approach can make the estimation accurate and does not decrease effective array aperture under a lower SNR and a smaller sampling number. Simulation results verify the correctness and efficiency of the new algorithm.
Nonparametric motion characterization for robust classification of camera motion patterns

Motion characterization plays a critical role in video indexing. An effective way of characterizing camera motion facilitates the video representation, indexing and retrieval tasks. This paper describes a novel nonparametric motion representation to achieve an effective and robust recognition of parts of the video in which camera is static, or panning, or tilting, or zooming, etc. This representation employs the mean shift filtering and the vector histograms to produce a compact description of a motion field. The basic idea is to perform spatio-temporal mode-seeking in the motion feature space and use the histograms-based spatial distributions of dominant motion modes to represent a motion field. Unlike most existing approaches, which focus on the estimation of a parametric motion model from a dense optical flow field (OFF) or a block matching-based motion vector field (MVF), the proposed method combines the motion representation and machine learning techniques (e.g., support vector machines) to perform camera motion analysis from the classification point of view. The main motivation lies in the impossibility of uniformly securing a proper parametric assumption in a wide range of video scenarios. The diverse camera shot sizes and frequent occurrences of bad OFF/MVF necessitates a learning mechanism, which can not only capture the domain-independent parametric constraints, but also acquire the domain-dependent knowledge to tolerate the influence of bad OFF/MVF. In order to improve performance, we can use this learning-based method to train enhanced classifiers aiming at a certain context (i.e., shot size, neighbor OFF/MVFs, and video genre). Other visual cues (e.g., dominant color) can also be incorporated for further motion analysis. Our main aim is to use a generic feature space analysis method to explore a flexible OFF/MVF representation in a nonparametric technique, which could be fed into a learning framework to robustly capture the global motion by incorporating the context information. Results on videos with various types of content (23 191 MVFs culled from MPEG-7 dataset, and 20 000 MVFs culled from broadcast tennis, soccer, and basketball videos) are reported to validate the proposed approach.
MAS Consensus and Delay Limits Under Delayed Output Feedback

In this technical note, we study the consensus problem for discrete-time multi-agent systems over an undirected, fixed network communication graph, focusing on the robustness of consensus with respect to communication delay. We assume that the agents' input is subject to a constant albeit possibly unknown time delay, and is generated by a distributed dynamic output feedback control protocol. Drawing upon concepts and techniques from robust control, notably those concerning gain margin optimization and analytic interpolation, we derive explicit, closed-form conditions for general linear agents to achieve consensus. Our results display an explicit dependence of the consensus condition on the delay value as well as on the agent's unstable poles and non-minimum phase zeros, showing that delayed communication between agents will generally hinder consensus and impose restrictions on the network topology. We also show that a lower bound on the maximal delay allowable for consensus can be computed by a simple line search method.
Research on the mode of emergency evolution

Whether an emergency happens depends on the input of the environment and the structure of the hazard bearing body. And the spread and chain-reacting of the emergency are based on the relation between the hazard bearing body and the emergency, also the characteristic of it, besides the structure of the hazard bearing body. Because of the complexity for the occurrence and evolvement of the emergency, we give a method that the chain-reacting network of emergencies shifts to a hierarchical network, and a layer stands for one kind of disaster. In this article, we analyze the characteristic of the hierarchical networks, and explain it with an example.
A generic mid-level representation for semantic video analysis

The paper presents a generic, mid-level representation for efficient semantic video analysis, which adopts a frame-by-frame scheme using P-frames rather than shot-based schemes. Each P-frame is partitioned into an m/spl times/n grid (row by column), and each cell is called a 'block'. The representation can bridge the semantic gap and build an intermediate description of video features across frames and blocks. Soccer video is used to showcase the potential of the framework for real video processing. Experiments with tennis video and news video have also been conducted. Results demonstrate the excellent performance of the framework in semantic analysis and also indicate its further potential for automatic video analysis.
An Attribute-Assisted Reranking Model for Web Image Search

Image search reranking is an effective approach to refine the text-based image search result. Most existing reranking approaches are based on low-level visual features. In this paper, we propose to exploit semantic attributes for image search reranking. Based on the classifiers for all the predefined attributes, each image is represented by an attribute feature consisting of the responses from these classifiers. A hypergraph is then used to model the relationship between images by integrating low-level visual features and attribute features. Hypergraph ranking is then performed to order the images. Its basic principle is that visually similar images should have similar ranking scores. In this paper, we propose a visual-attribute joint hypergraph learning approach to simultaneously explore two information sources. A hypergraph is constructed to model the relationship of all images. We conduct experiments on more than 1,000 queries in MSRA-MMV2.0 data set. The experimental results demonstrate the effectiveness of our approach.
Collaborative Index Embedding for Image Retrieval

In content-based image retrieval, SIFT feature and the feature from deep convolutional neural network (CNN) have demonstrated promising performance. To fully explore both visual features in a unified framework for effective and efficient retrieval, we propose a collaborative index embedding method to implicitly integrate the index matrices of them. We formulate the index embedding as an optimization problem from the perspective of neighborhood sharing and solve it with an alternating index update scheme. After the iterative embedding, only the embedded CNN index is kept for on-line query, which demonstrates significant gain in retrieval accuracy, with very economical memory cost. Extensive experiments have been conducted on the public datasets with million-scale distractor images. The experimental results reveal that, compared with the recent state-of-the-art retrieval algorithms, our approach achieves competitive accuracy performance with less memory overhead and efficient query computation.
Iterative Graph Seeking for Object Tracking

To effectively solve the challenges in object tracking, such as large deformation and severe occlusion, many existing methods use graph-based models to capture target part relations, and adopt a sequential scheme of target part selection, part matching, and state estimation. However, such methods have two major drawbacks: 1) inaccurate part selection leads to performance deterioration of part matching and state estimation and 2) there are insufficient effective global constraints for local part selection and matching. In this paper, we propose a new object tracking method based on iterative graph seeking, which integrate target part selection, part matching, and state estimation using a unified energy minimization framework. Our method also incorporates structural information in local parts variations using the global constraint. We devise an alternative iteration scheme to minimize the energy function for searching the most plausible target geometric graph. Experimental results on several challenging benchmarks (i.e., VOT2015, OTB2013, and OTB2015) demonstrate improved performance and robustness in comparison with existing algorithms.
MIMU and Its Application in Mobile Robot's Self-localization

This paper introduces principal of micro inertial measurement unit (MIMU), designs a arrange form of MIMU. According to lots of experiment data, the gyroscope's drift and accelerometer's warp are analyzed, and provides an AR(1) model. After Kalman filtering, the simulation results show that the bias of MIMU reduces to one tenth of the original, and prove this methodological feasible.
Cascaded Interactional Targeting Network for Egocentric Video Analysis

Knowing how hands move and what object is being manipulated are two key sub-tasks for analyzing first-person (egocentric) action. However, lack of fully annotated hand data as well as imprecise foreground segmentation make either sub-task challenging. This work aims to explicitly ad dress these two issues via introducing a cascaded interactional targeting (i.e., infer both hand and active object regions) deep neural network. Firstly, a novel EM-like learning framework is proposed to train the pixel-level deep convolutional neural network (DCNN) by seamlessly integrating weakly supervised data (i.e., massive bounding box annotations) with a small set of strongly supervised data (i.e., fully annotated hand segmentation maps) to achieve state-of-the-art hand segmentation performance. Secondly, the resulting high-quality hand segmentation maps are further paired with the corresponding motion maps and object feature maps, in order to explore the contextual information among object, motion and hand to generate interactional foreground regions (operated objects). The resulting interactional target maps (hand + active object) from our cascaded DCNN are further utilized to form discriminative action representation. Experiments show that our framework has achieved the state-of-the-art egocentric action recognition performance on the benchmark dataset Activities of Daily Living (ADL).
Musical genre classification using support vector machines

Automatic musical genre classification is very useful for music indexing and retrieval. In this paper, an efficient and effective automatic musical genre classification approach is presented. A set of features is extracted and used to characterize music content. A multi-layer classifier based on support vector machines is applied to musical genre classification. Support vector machines are used to obtain the optimal class boundaries between different genres of music by learning from training data. Experimental results of multi-layer support vector machines illustrate good performance in musical genre classification and are more advantageous than traditional Euclidean distance based method and other statistic learning methods.