nils-herrmann/clean_scirepeval huggingface dataset:
Part-based initialization for hand tracking

Initializing hand/finger articulation for tracking is a very challenging problem, mainly because hand articulation is complicated and it has a large number of degrees of freedom. Most existing algorithms initialize tracking manually, or use a nearest-neighbor search with restricting the number of possible hand gestures. This paper presents a new solution to this problem by increasing the dimensionality but taking advantage of the sparseness. The basic idea is to divide the set of phalange joint angles into many overlapping subsets. As each subset has a much smaller number of joint angles, it is much easier to design a smaller-scale articulation estimator. The estimation of the whole hand is done by the collaboration of a network of dependent smaller-scale estimators. This paper describes a novel way of designing the smaller-scale estimators as well as a principled way of fusing the estimates. A tracking system is also shown by using this initialization technique.
What Are We Tracking: A Unified Approach of Tracking and Recognition

Tracking is essentially a matching problem. While traditional tracking methods mostly focus on low-level image correspondences between frames, we argue that high-level semantic correspondences are indispensable to make tracking more reliable. Based on that, a unified approach of low-level object tracking and high-level recognition is proposed for single object tracking, in which the target category is actively recognized during tracking. High-level offline models corresponding to the recognized category are then adaptively selected and combined with low-level online tracking models so as to achieve better tracking performance. Extensive experimental results show that our approach outperforms state-of-the-art online models in many challenging tracking scenarios such as drastic view change, scale change, background clutter, and morphable objects.
Design and implementation for an automotive DOA system

In order to solve the problem of traffic accidents that caused by drivers and passengers open the door abruptly, this paper proposes a door open alarm (DOA) system that uses the camera as the sensor, and applies two analog cameras that set at the both side view mirrors to get the video information. This paper is based on image processing to develop the algorithm and has been implemented on Renesas®R-Car developed platform that built in ARM®Cortex-A15 dual-core and clock rate is 1.5 GHz. The DOA system can detect closing targets in 20 meters of the rear vehicle immediately, and the frame rate is 30 frame per second (FPS). The detection rate in day and night is 98.04% and 96.15%.
A 40-nm-Gate InAs/In0.7Ga0.3As Composite-Channel HEMT with 2200 mS/mm and 500-GHz fT

A 40-nm T-gate high-electron-mobility-transistor with InAs/In<inf>0.7</inf>Ga<inf>0.3</inf>As composite-channel has been fabricated. The device exhibits a transconductance (g<inf>m</inf>) of 2200 mS/mm, a cutoff frequency f<inf>T</inf> of 506 GHz and a minimum noise figure of 1.21 dB at a frequency of 58 GHz. These performances make the device well-suited for millimeter-wave or sub-millimeter-wave applications.
A Novel Output Power Maximizing Timing Recovery Algorithm for Rectangle QAM

A timing recovery algorithm has been proposed in this paper. Johnson proposed output power maximizing timing recovery algorithm which was applied to QPSK signal. The novel algorithm can be applied to QAM signal over AWGN and multipath fading channels. It is easily implemented as it requires only one sample per symbol for its operation. The simulation results show that the proposed algorithm needs no training data with fast convergence.
Code constructions for nonasynchronous-CDMA systems

Code constructions for quasi-synchronous and asynchronous code-division multiple-access (CDMA) systems with finite delays are presented in this paper. With all transmitted sequences reaching the receiver within a finite delay of D digits, for each J, there are at least 2/sup J/(J-2)/(2log/sub 2/3)-1+1/log/sub 2/3 and (J-1)2/sup J-2//log/sub 2/3 spreading sequences of length 2/sup J-1/D+2/sup J/-1 for quasi-synchronous and asynchronous systems respectively.
A New Method for Test Suite Reduction

Test suite reduction is to find a subset of the test suite containing a minimal number of test cases that can satisfy all test requirements. Test suite reduction techniques attempt to remove redundant test cases. Existed minimal set selection methods cannot guarantee the optimality of representative sets obtained. This paper adapts the Quine-McCluskey algorithm to find the minimal representative set. To demonstrate the applicability of the approach, we conduct an experimental comparison. The results show that the Quine-McCluskey approach can identify the optimal test suite. Meanwhile, the application of this method for testing the automatic fare collection systems of Shanghai Metro also validated that it is effective.
Lattice and dynamic aperture of the Duke FEL storage ring

A low emittance lattice of the Duke FEL (Free Electron Laser) storage ring dedicated to drive UV-VUV FELs is presented. The second order geometrical aberration is compensated to increase the dynamic aperture. The 6-D dynamic aperture study has been performed on this lattice using symplectic tracking codes, which demonstrates a large energy aperture. The influences of higher-order multipoles (HMs), RMS errors and 6.8-m OK-4 undulator on dynamic aperture are studied. The transverse dynamic aperture is mainly limited by HMs in the straight section quadrupoles. For future FEL operations, we have designed a new straight section lattice for a 26-m long FEL undulator. The dynamic aperture for this lattice is discussed.<<ETX>>
Single-to-balanced BAW filter combining CRF with lattice filter

Although FBAR filters have successfully penetrated the CDMA duplexer market, they still cannot displace the SAW filters in traditional RF front-end filter applications. Most of present cell phone systems like GSM or WCDMA require the filter device to have both single- to-balanced conversion and impedance transformation functions. Current BAW filter with ladder or lattice topology cannot satisfy any of these requirements. Therefore the coupled resonator filter (CRF) has gained a lot of attention since it posses the potential to meet all the requirements of state-of-the-art wireless systems. In the past, CRF was fabricated in a SMR structure with proper selection of reflector layer materials. The reason of using the SMR structure is to suppress the redundant resonant response of the CRF. However, comparing with a membrane-type structure, the SMR has slightly lower Q and effective K 2 . In this paper, a new design structure combining CRF and lattice filter for single-to-balanced conversion applications is proposed. It can help to eliminate the need of SMR structure and thus the performance could be improved. This concept is verified by simulation and the comparison to conventional ones is discussed.
A kind of topology control algorithm aimed at extending the life cycle of the WSN

The current classic topology control algorithm based on power control class cannot be effective in balancing the network node energy consumption. This defect involves two factors: communication energy consumption between nodes and the node residual energy, which leads to the establishment of communication link cost model. A kind of topology control algorithm is proposed based on the initiative to balance the node energy consumption and extending the network lifetime, as well as the given theoretical and simulation analysis. The results showed that the algorithm enables the adjustment of node power, dynamic computing on the communication link cost, dynamic optimization of each node of the network topology, capable of achieving balance network energy consumption and extend the life cycle of wireless sensor networks (WSN).
Characteristics of Pd-Rh/CeO2/Al2O3 TWC for NOx and PAHs removal in flue gas of MSWI

Pd-Rh/CeO<inf>2</inf>/Al<inf>2</inf>O<inf>3</inf> three-way catalysts by sol-gel method were investigated by catalytic performance test for simultaneous removals NO<inf>x</inf> and PAHs. The experiments were carried out in a laboratory scale incineration system equipped with a catalyst reactor. The catalysts were characterized with BET, SEM, and XRD. The sol-gel way prepared carrier CeO<inf>2</inf>/Al<inf>2</inf>O<inf>3</inf>, characterization results showed that, CeO<inf>2</inf>/Al<inf>2</inf>O<inf>3</inf> the specific surface area is 207m<sup>2</sup>/g, average pore size is 88.76 Å, pores volume is 0.67 cm<sup>3</sup>/g. In addition to surface roughness, the surface also appeared in many small crystal particles, suggesting that these small crystals may be Pd crystals. Experimental results showed that the removal efficiency of NO<inf>x</inf>, and PAHs in Pd-Rh/CeO<inf>2</inf>/Al<inf>2</inf>O<inf>3</inf> TWC was generally greater than 80%. The bimetallic catalyst with a Pd/Rh ratio of 1/0.10 was found to have a best activity for highest removal efficiency.
Detecting and Aligning Faces by Image Retrieval

Detecting faces in uncontrolled environments continues to be a challenge to traditional face detection methods due to the large variation in facial appearances, as well as occlusion and clutter. In order to overcome these challenges, we present a novel and robust exemplar-based face detector that integrates image retrieval and discriminative learning. A large database of faces with bounding rectangles and facial landmark locations is collected, and simple discriminative classifiers are learned from each of them. A voting-based method is then proposed to let these classifiers cast votes on the test image through an efficient image retrieval technique. As a result, faces can be very efficiently detected by selecting the modes from the voting maps, without resorting to exhaustive sliding window-style scanning. Moreover, due to the exemplar-based framework, our approach can detect faces under challenging conditions without explicitly modeling their variations. Evaluation on two public benchmark datasets shows that our new face detection approach is accurate and efficient, and achieves the state-of-the-art performance. We further propose to use image retrieval for face validation (in order to remove false positives) and for face alignment/landmark localization. The same methodology can also be easily generalized to other face-related tasks, such as attribute recognition, as well as general object detection.
Development of CAN Network for VMS Based on XC164CS

In the design of modern vehicles, CAN-bus network has been applied for the information exchange among the ECUs of the vehicle. The CAN communication network for vehicle management system (VMS) is designed based on XC164CS microcontroller in the paper. The characteristics and construction of Twin CAN module of XC164CS were introduced. The principle of time triggered controller area network (TTCAN) is described. The hardware interface circuit is developed to increase the stability and safety of the CAN nodes. The drive software and multitask application is realized on the platform of embedded operation system μC/OS-II. This CAN bus network can improve the anti-jamming and dependability effectively, which was applied to control and exchange the information of the vehicles.
Measuring Customer Satisfaction based on Neural Networks Partial Least Squares Approach

Customer satisfaction measurement is an important part of marketing research in industrial organizations since it is the key to formulating customer value strategies and to continuously improving implementation of these strategies. Traditional techniques for modeling the network such as partial least squares (PLS) lack the capability of fitting the nonlinear and asymmetric relationships. This article presents a new technique of neural networks partial least squares (NNPLS) to measure customer satisfaction. The details of NNPLS are discussed. The results show that the NNPLS gives the smaller prediction errors compared with linear PLS. Therefore a robust model expressed by NNPLS succeeds in correlating the relations between customer satisfaction, customer loyalty and their drivers
ESB-DMR beamforming under random matrix theory judgment principle

Eigenspace-based beamformer and dominant mode rejection are robust against DOA mismatch and array model mismatch when SNR is proper. While the former algorithm suffers from performance break down at low SNR and at high SNR, the performance of the latter degrades. We propose a principle to judge the level of the SNR based on random matrix theory. The idea of random matrix theory is to identify the level of subspace swap phenomenon, which is closely related to input SNR. With the principle we are able to combine two methods together and both effectiveness and robustness of proposed principle are demonstrated by simulation results.
A multi-label learning method for efficient affective detection

Biosignals-based affective computing plays an important role in human-computing interaction. In recent years, a lot of works have been successfully implemented for emotion recognition with biological signals. However most of them are computationally expensive due to the complexity of models. In this paper, we present a multi-label learning (MLL) method to map biological signals to an affective model in real time. Multi-label learning combines multiple classifiers in a same training process by evaluating correlations between labels. To evaluate the proposed model, 25 male subjects were recruited to anticipate an experiment for model evaluation. Dynamic images were specifically chosen to elicit emotion described by different arousal and valence levels in the experiment. In terms of physiological signals, electrocardiogram (ECG) and skin conductivity (SC) signals were collected for classification. By applying the MLL method to analyze the relationships between physiological signal features and affective labels, we observed that the proposed method performed better than traditional method, which showing the potential of MLL in affective detection.
Mining Object Parts from CNNs via Active Question-Answering

Given a convolutional neural network (CNN) that is pre-trained for object classification, this paper proposes to use active question-answering to semanticize neural patterns in conv-layers of the CNN and mine part concepts. For each part concept, we mine neural patterns in the pre-trained CNN, which are related to the target part, and use these patterns to construct an And-Or graph (AOG) to represent a four-layer semantic hierarchy of the part. As an interpretable model, the AOG associates different CNN units with different explicit object parts. We use an active human-computer communication to incrementally grow such an AOG on the pre-trained CNN as follows. We allow the computer to actively identify objects, whose neural patterns cannot be explained by the current AOG. Then, the computer asks human about the unexplained objects, and uses the answers to automatically discover certain CNN patterns corresponding to the missing knowledge. We incrementally grow the AOG to encode new knowledge discovered during the active-learning process. In experiments, our method exhibits high learning efficiency. Our method uses about 1/6&#x2013;1/3 of the part annotations for training, but achieves similar or better part-localization performance than fast-RCNN methods.
An LTCC Bluetooth antenna with symmetric structure

An LTCC Bluetooth antenna with symmetric structure is proposed in this paper. The proposed antenna is designed for W-LAN (IEEE 802.11b). It has a symmetric structure and it is in a ceramic chip with high permittivity. The characteristics of antenna (frequency, far field gain, return loss) are suitable for the Bluetooth application of modern wireless communication systems. The influence of different parameters is discussed. The antenna has a single feed with total compact size 9×2×1mm3.
Customer satisfaction measurement model of e-government service

Since the late 1990s, numerous governments have made huge investments in electronic government services. The rise of these services has led to a concurrent need to develop models of customer satisfaction of e-government services. This study aims to establish an e-government customer satisfaction measurement model from the behavioral perspectives of users. Based on the Technology Acceptance Model (TAM) and Self-service Technology (SST), the study proposes an intergraded model for measuring e-government customer satisfaction. An online survey is undertaken. The target population is undergraduate students who have experienced with browsing and searching for the information in e-government web portals. Using structural equation modeling, a highly statistically significant goodness-of-fit was found for the model. The results show that the important determinants of customer satisfaction of e-government are perceived usefulness, perceived ease of use, perceived reliability and perceived personalization. Finally, the academic and practical conclusions are discussed.
Status of the Booster Injector for the Duke FEL Storage Ring

This paper presents the current status of the booster synchrotron for the Duke Free Electron Laser Laboratory (DFELL) storage ring. The booster will provide full energy injection into the storage ring in a wide energy range from 0.27 to 1.2 GeV. When operating the DFELL storage ring as the High Intensity Gamma Source (HIGS) to produce gamma photons above 20 MeV with Compton scattering, continuous electron loss occurs. The top-off mode operation of the booster injector will enable the continuous operation of the HIGS facility by replenishing the lost electrons. The design requirement for a compact booster with the single bunch extraction capability remains a challenge for the machine development. Presently, the booster project is in the installation phase. The magnetic elements, vacuum chambers, injection and extraction kickers have been developed in collaboration with and fabricated at the Budker Institute of Nuclear Physics (BINP), Russia. The diagnostic and control system is being developed at DFELL. The commissioning of the booster synchrotron will start at the end of 2005.
Tracking Motion-Blurred Targets in Video

Many emerging applications require tracking targets in video. Most existing visual tracking methods do not work well when the target is motion-blurred (especially due to fast motion), because the imperfectness of the target's appearances invalidates the image matching model (or the measurement model) in tracking. This paper presents a novel method to track motion-blurred targets by taking advantage of the blurs without performing image restoration. Unlike the global blur induced by camera motion, this paper is concerned with the local blurs that are due to target's motion. This is a challenging task because the blurs need to be identified blindly. The proposed method addresses this difficulty by integrating signal processing and statistical learning techniques. The estimated blurs are used to reduce the search range by providing strong motion predictions and to localize the best match accurately by modifying the measurement models.
A Study of Integrated Optical Design and Optimization for LED Backlight Module With Prism Patterns

In backlight modules, the light guide plate (LGP) is a key component for performance and also facilitates access to develop LGPs on their own. In this research, we propose a newly developed method: LEDs with freeform secondary optical lens as a lighting source are employed to integrate and manipulate the specially designed and optimized 3D-like pattern distribution of the micro features to obtain the required optical characteristics at maximal performance. In this research, a LGP of 3.5 inch dimensions as an example of such an integral optical design and optimization of LGPs is expected not only to remove optical films but also reduce the thickness of the backlight module and increase overall brightness and uniformity. After integral optical design, optimization extended by the Taguchi with principal component analysis method improved the brightness by up to 98%, reduced the thickness by 10% and improved the uniformity by 62.5%.
Incorporate discriminant analysis with EM algorithm in image retrieval

One of the difficulties of content-based image retrieval (CBIR) is the gap between high-level concepts and low-level image features, e.g., color and texture. Relevance feedback was proposed (Rui et al., 1999 to take into account the above characteristics in CBIR. Although relevance feedback incrementally supplies more information for fine retrieval, two challenges exist: the labeled images from the relevance feedback are still very limited compared to the large unlabeled images in the image database; and relevance feedback does not offer a specific technique to automatically weight the low-level feature. In this paper, image retrieval is formulated as a transductive learning problem by combining unlabeled images in supervised learning to achieve better classification. Experimental results show that the proposed approach has a satisfactory performance for image retrieval applications.
An Algorithm for Calculating Access Time Windows of Spot Targets Based on Space Geometry Model

STK software is usually used to calculate access time windows for observing spot targets in aerospace, but it's difficult to develop some applications based on STK. In this paper, we present a novel algorithm for calculating roll angles and access time windows of spot targets based on space geometry model. The algorithm can quickly check the visibility of spot targets, and get roll angles and access time windows if they are visible. Experimental results show that the algorithm is effective.
Cosegmentation and Cosketch by Unsupervised Learning

Co segmentation refers to the problem of segmenting multiple images simultaneously by exploiting the similarities between the foreground and background regions in these images. The key issue in co segmentation is to align common objects between these images. To address this issue, we propose an unsupervised learning framework for co segmentation, by coupling co segmentation with what we call ``co sketch''. The goal of co sketch is to automatically discover a codebook of deformable shape templates shared by the input images. These shape templates capture distinct image patterns and each template is matched to similar image patches in different images. Thus the co sketch of the images helps to align foreground objects, thereby providing crucial information for co segmentation. We present a statistical model whose energy function couples co sketch and co segmentation. We then present an unsupervised learning algorithm that performs co sketch and co segmentation by energy minimization. Experiments show that our method outperforms state of the art methods for co segmentation on the challenging MSRC and iciest datasets. We also illustrate our method on a new dataset called Coseg-Rep where co segmentation can be performed within a single image with repetitive patterns.
Formation and electrochemical behaviour of polyoxometalate-capped gold nanoparticles assembly electrode

The functionalized gold nanoparticles capped with polyoxometalates were prepared by a simple photoreduction technique in the presence of phosphododecamolybdate (PMo12) to serve as reducing reagents, photocatalysts, and stabilizers. Monolayer and multilayer structures of the negatively charged PMo12-capped gold nanoparticles (PMo12-NGs) were constructed on a polyvinylpyridine-derivatized indium-doped tin oxide (ITO) electrode through layer-by-layer self-assembly technique. The PMo12-NGs modified ITO electrode exhibits the characteristic electrochemical behaviour of the surface confined PMo12: multi-step reversible redox reactions. Excellent catalytic activity for the reduction of iodate was obtained on the modified electrode.