nils-herrmann/clean_scirepeval huggingface dataset:
Mode coupling strength in a microsphere cavity coupled with fiber taper

With the coupled-mode theory, we analyzed the energy transmission between the microsphere cavity and waveguide system, and revealed the mode coupling strength in a bare microsphere coupled with a fiber taper theoretically and experimentally. In a practical taper-microsphere coupling system, the traveling wave clockwise (CW) and counterclockwise (CCW) whispering gallery modes (WGMs) should existed simultaneity. Those two modes are coupled in the microsphere cavity with a coupling strength (beta). In experiment, the reflection spectra of the taper-microsphere system were investigated, By modifying various coupling condition for microspheres, different groups of data were obtained from the spectra, and used as fitting data for coupling strength estimation. The theory will be valuable in evaluating the upper limit of quality factor (Q) of a microcavity basing on the WGMs. Referring to the result and consequence of beta, it enables multiple ways to improve the Q-factor of a real cavity like annealing. Because the experiment device is compact and sensitive, such method is also promising for detecting the propagating loss in fiber instead of traditional way.
Non-resonant electrostatic energy harvester for wideband applications

Presented is a novel non-resonant electrostatic energy harvester targeted at the wideband frequency response for micro-system powering. To boost non-resonance power output under low frequency, the vertical gap changing method is adopted to induce variable charges, which generates approximately 17 times of charge change compared with the conventional method of laterally changing overlap area (at 1 mm gap). Further, to boost the low frequency and non-resonant power generating capability, the differential structure is adopted to achieve doubled output voltages. Testing results reveal that optimal output power occurs when the inner plate is of large effective movement range and large relative displacement with surrounding electrets. Maximally, 3.9–7.0 V voltage and 0.2–0.7 μW power are achieved over the 5–120 Hz frequency range.
The Forensics for Detecting Manipulation on Part of Text

The adventure of sophisticated photo editing software has made it increasingly easier to manipulate text in digital images. Visual inspection cannot definitively distinguish the forgeries from authentic photographs. In the imaging process, as defocusing, atmospheric turbulence, diffraction and the defects of image device, digital image can't reproduce the details of texture perfectly, while the text image that obtained by photo-editing software will do better, and will show no difference with the authentic image in the appearance. In this paper, we describe a new forensics technique that focuses on manipulation on part of text. By decomposition of the text and to extract the characteristics of edge points in text image, we use support vector machine (SVM) to training classification model which is used for the identification of the authenticity of text message in images. Our test result indicates that this method can detect the manipulation on part of text, and the general detection rate is promised.
Design and implementation of an AADL model parser

In this paper, we focus on the problem that analysis and verification tools' inconvenience to extract textual AADL architecture model, and put forward AADL model parser which converts AADL architecture model to AADL architecture model object. Based on AADL meta-model, we use EMF to generate the classes of AADL elements, while using ANTLR to implement the lexer and parser of AADL. And then we implement the AADL model parser based on Eclipse IDE. Meanwhile, this paper presents a case study on the modeling of an autopilot system to show the application scenario of AADL model parser.
Hierarchical spatio-temporal context modeling for action recognition

The problem of recognizing actions in realistic videos is challenging yet absorbing owing to its great potentials in many practical applications. Most previous research is limited due to the use of simplified action databases under controlled environments or focus on excessively localized features without sufficiently encapsulating the spatio-temporal context. In this paper, we propose to model the spatio-temporal context information in a hierarchical way, where three levels of context are exploited in ascending order of abstraction: 1) point-level context (SIFT average descriptor), 2) intra-trajectory context (trajectory transition descriptor), and 3) inter-trajectory context (trajectory proximity descriptor). To obtain efficient and compact representations for the latter two levels, we encode the spatiotemporal context information into the transition matrix of a Markov process, and then extract its stationary distribution as the final context descriptor. Building on the multichannel nonlinear SVMs, we validate this proposed hierarchical framework on the realistic action (HOHA) and event (LSCOM) recognition databases, and achieve 27% and 66% relative performance improvements over the state-of-the-art results, respectively. We further propose to employ the Multiple Kernel Learning (MKL) technique to prune the kernels towards speedup in algorithm evaluation.
Design of a SIMD multimedia SoC platform

PLX is a native SIMD instruction set architecture, which integrates scalar instructions into a multimedia extension SIMD core to run OS and multimedia functions. It changes the typical CPU-DSP dual core architecture to a SIMDI/OCTRL architecture. We have developed a PLX-based system developing platform, including a PLX core, a RISC-like 8051 as I/O controller, an H.264 accelerator hardware, a SystemC system level virtual prototype for verification and OS development at early stage, and a SIMD code generation methodology for software to better exploit the 64-bit SIMD computation power.
Video eCommerce++: Toward Large Scale Online Video Advertising

The prevalence of online videos provides an opportunity for e-commerce companies to recommend their products in videos. In this paper, we propose an online video advertising system named Video eCommerce ++, to exhibit appropriate product ads to particular users at proper time stamps of videos, which takes into account video semantics, user shopping preference, and viewing behavior feedback. First, an incremental co-relation regression (ICRR) model is novelly proposed to construct the semantic association between videos and products. To meet the requirement of online advertising, ICRR is implemented in an incremental way to reduce the time complexity. User preference diffusion (UPD) is induced under the framework of heterogeneous information network to construct user-product association from two different e-commerce platforms, Tmall and MagicBox, which alleviates the problems of data sparsity and cold start. A video scene importance model (VSIM) is proposed to model the scene importance by utilizing the user viewing behavior, so that ads can be embedded at the most attractive positions in the video stream. To combine the outputs of ICRR, UPD, and VSIM, a unified distributed heterogeneous relation matrix factorization (D-HRMF) is applied for online video advertising, which is efficiently conducted in parallel to address the real-time update problem, so that the whole system can be performed in real time. Extensive experiments conducted on a variety of online videos from Tmall MagicBox demonstrate that Video eCommerce++ significantly outperforms the state-of-the-art advertising methods, and can handle large-scale data in real time.
Remote controlling for ring-type traveling ultrasonic motor based on zigbee

A remote controlling circuit of ring-type traveling ultrasonic motor based on zigbee technique has been proposed, designed and verified in this paper. The mesh network has been calculated by the algorithm named the cluster-tree during address assignment according to the zigbee protocol. The simulation and the experiment have been shown that the circuit with wireless control for the ring-type traveling ultrasonic motor can realize the adjustment of the speed, the torque and other control algorithm of the USM.
A new algorithm of uncorrelated discriminant vectors and its application

A study has been made on the method of solving an optimal set of uncorrelated discriminant vectors. We have made an improvement to the method of solving an optimal set of uncorrelated discriminant vectors of Zhong Jin et al. (2001) on the basis of the equivalence of two Fisher criteria. A concise formula for solving the optimal set of uncorrelated discriminant vectors has been proposed. Numerical experiments on the facial database of ORL show the effectiveness of the proposed method.
Noncoherent reduced state differential sequence detection of continuous phase modulation

A noncoherent reduced state differential sequence detection (RSDSD) for continuous phase modulation (CPM) is proposed in this paper. This method makes use of the reduced state trellis based on reduced state sequence detection (RSSD) and performs one-symbol differential Viterbi detection. The minimum Euclidean distance of the reduced state trellis is analyzed for full response CPM and partial response CPM. Bit error rates of RSSD and RSDSD are simulated for octal 2RC signal with h=1/8 in the AWGN channel and comparisons are made between the different RSSD schemes and between coherent RSSD and noncoherent RSDSD with Doppler frequency shifts. Although our proposed scheme is degraded by 2 dB at 10/sup -3/ bit error rate as compared with coherent detection, it is more appropriate and robust when the carrier recovery is difficult.
Main cause of resonance appeared around 7.5GHz on the frequency response of S-parameters of PWB

Hardware engineers engaging in the mobile system pursue lower power, less weight and higher performance by minimizing the main factors impacting the Printed-Wiring-Board (PWB) performance which involve a large number of discontinuities. It happens to meet the abnormal phenomenon like a resonance, even in the PWB comprised of only the transmission lines connected one after another in series with via-junctions of 10. However it is not encountered in a case connected by a few via-junctions, and the field solver used ordinarily for checking the abnormality would not predict it. The main cause of the resonance appeared around 7.5 GHz on the frequency response of the S-parameters of the PWB with plural transmission lines connected one after another in series by the via-junctions was investigated by the net work analyzer and an equivalent circuit model. And the characteristic impedance mismatches among the transmission lines was found out to be the main factor for this phenomenon.
The Quasi Parallel-Sided Sound Focusing Lens

is much smaller compared with fl, f and the aperture of the lens, and that some of the sound beams projected by the source pass discussion about the computing method of through the medium surrounding the lens and the lens in the cases of the point source finally reach the lens surface which faces (caee 1) and the parallel sound beme (caee the source* discuss the O-th and 2) incident on its step aide, and derived the n-th sound beams only, and the rest thout the calculating formulas of the lens en can be deduced in the same way.
On the Annotation of Web Videos by Efficient Near-Duplicate Search

With the proliferation of Web 2.0 applications, user-supplied social tags are commonly available in social media as a means to bridge the semantic gap. On the other hand, the explosive expansion of social web makes an overwhelming number of web videos available, among which there exists a large number of near-duplicate videos. In this paper, we investigate techniques which allow effective annotation of web videos from a data-driven perspective. A novel classifier-free video annotation framework is proposed by first retrieving visual duplicates and then suggesting representative tags. The significance of this paper lies in the addressing of two timely issues for annotating query videos. First, we provide a novel solution for fast near-duplicate video retrieval. Second, based on the outcome of near-duplicate search, we explore the potential that the data-driven annotation could be successful when huge volume of tagged web videos is freely accessible online. Experiments on cross sources (annotating Google videos and Yahoo! videos using YouTube videos) and cross time periods (annotating YouTube videos using historical data) show the effectiveness and efficiency of the proposed classifier-free approach for web video tag annotation.
Energy conservation approach for data center cooling using heat pipe based cold energy storage system

In the present paper, design and economics of the novel type of thermal control system for data center cooling using heat pipe based cold energy storage system has been proposed and discussed. Two types of cold energy storage system namely: Ice storage system and cold water storage system are explained and sized for datacenter with heat output capacity of 8800 KW. Basically, the cold energy storage will help to downsize the chiller and decrease its runtime that will save electricity related cost and decrease green house gas emissions from the electricity generation. The proposed cold energy storage system can be connected in the existing datacenter facilities without major design changes. Out of the two proposed systems, ice based cold energy storage system is mainly recommended for small to medium size datacenters which are located in very cold locations and offers long term seasonal storage facility. Water based cold energy storage system provides more compact size with short term storage (hours to days) and is potential for both small to large size datacenters with yearly average temperature below the cold storage water temperature (~ 25°C). The cold water storage system is sized on the basis of metrological conditions in Poughkeepsie, USA. As an outcome of the thermal and cost analysis, an optimum size of cold energy storage system should be designed to handle 60% of the yearly datacenter load. Preliminary results obtained from the experimental system design to test the ice formation potential of the heat pipe based cold energy storage system has shown good result and validated the proposed concept.
SA Optimizing Algorithm of SVM Super-Parameters

Based on the statistical learning theory support vector machine focuses on the machine learning strategies under small samples and gets better generalization ability than those tools based on the experience risk minimization principle. Its classing or regression performance will be affected by relative super-parameters. An improved multi object optimization algorithm based on simulated annealing is proposed and applied to super-parameters optimization of the support vector machine with a RBF kernel. Then selection of proper searching space, initial feasible solution,initial temperature and design of an optimal object function are discussed in detail. The validation on the some standard datasets is carried out and its feasibility and effectiveness are confirmed.
Directivity Analysis of a Novel Piezoelectric Film Based Ultrasonic Microspeaker Array

In this paper, a novel piezoelectric film based ultrasonic microspeaker array is proposed. The SiO2/Pt/Pb(Zr,Ti)O3 /Pt/Ti/SiO2 multimorph structure has been designed and fabricated using silicon based micro-machining process. The membrane size of the single transducer cell is 1000times1000times2.5 mum 3, and the main resonance frequency is located near 68kHz. The performance of directivity is a very important parameter in microspeaker array. Especially in ultrasonic applications, a good transmitting directivity of microspeaker array is indispensable. Dissimilar arrangement of the cells in transducer array could cause different directivity performance. After comparison, when achieving a good transmitting directivity, the square combined plane array has the smallest array area. In addition, the directivity patterns of different planes vertical to the microspeaker array plane are also discussed
Kinect Based Dynamic Hand Gesture Recognition Algorithm Research

In dynamic gesture track recognition, gesture recognition rate has always been a problem, Because the traditional hand gesture recognition in how to separate the hand type from the surrounding environment, and then determine the palm position has been one of the difficult problems. This paper presents a Kinect based dynamic gesture trajectory identification method, use OpenNI where palm is defined as a Kinect node, quickly and accurately get the palm position coordinates, and then use the HMM technology to do gesture model training to improve the recognition rate. The experimental results show that this method of gesture track recognition rate is higher than the traditional.
Characterization of capacitor charging by vibration-based piezoelectric power generator

Mechanical vibration energy can be converted into electrical energy using piezoelectric power generator. The electrical energy is usually collected by a storage capacitor instead of being directly consumed by the load. In this way, to characterize the charging of the storage capacitor is important and necessary. This paper focuses on the characterization of the piezoelectric power generator with capacitive load. Analytical expression of the voltage across the storage capacitor is derived and analyzed. Analysis results show that the storage capacitor is exponentially charged and the maximum instant power occurs when one-half the maximum voltage of the storage capacitor. These analytical results are well conformed by experiments. They are useful for both macro and MEMS-based energy-harvesting devices.
A highly efficient switched-capacitor LED driver with switching frequency hopping technique

A novel frequency hopping technique is proposed to increase the efficiency in a switched-capacitor LED driver, based on analyzing the dependence of power loss on switching frequency. LEDs' load current is featured uniquely by jumping periodically from a constant value (20mA) to zero for dimming function. Developed on an improved VCO, the hopping technique makes the switching frequency vary discretely at pre-defined levels in accordance with load current, whose basic principle is that heavier the load, higher the frequency (200KHz to 1MHz). By elimination of current-sensing resistors and other optimization measures, the efficiency is further improved. The circuit is designed and simulated in TSMC 0.35µm CMOS process. Simulation results show the overall efficiency is increased by 5% on average in comparison with fixed high frequency ones.
Optimizing Sequence of Enhanced External Counter-Pulsation during AEI-CPR Based on Genetic Algorithm

Enhanced External Counter-Pulsation (EECP) was lower limbs-compression equipment for cardiopulmonary resuscitation (CPR) that enhanced blood regurgitation during recoil phase of chest compression. This paper describes optimizing sequence of the EECP for a CPR technique - Active Compression-Decompression CPR with EECP and Inspiratory Impedance Threshold Valve (AEI-CPR). A mathematical model of human circulatory system used to research AEI-CPR has been established. And then the hemodynamic effects of AEI-CPR to the blood circulatory system model were performed on the model. To improve the aortic pressure and myocardial perfusion pressure during cardiac arrest, an optimization algorithm, based on genetic algorithm, were used to find optimum sequence of the EECP when other parameters of external force for AEI-CPR were definite. Applying the optimization algorithm on the mathematical model, the optimum sequence of the EECP had been found. And the experiment results indicated that obvious hemodynamic effects were attained when the EECP began compression at the end of chest compression.
Contextual constraints based kernel discriminant analysis for face recognition

This paper proposes a two-step subspace learning framework by combining non-linear kernel PCA (KPCA) and with contextual constraints based linear discriminant analysis (CCLDA) for face recognition. The linear CCLDA approach does not consider the higher order non-linear information in facial images, whereas the wide face variations posed by some factors, such as viewpoint, illumination and expression, existing in nonlinear subspaces may lead to many difficulties in face recognition and classification problems. To counteract the above problem, we incorporate the contextual information into kernel discriminant analysis by using KPCA in a two-step process, which provides more useful information for face recognition and classification. Experimental results on three well-known face databases, ORL, Yale and XM2VTS, validate the effectiveness of the proposed method. (4 pages)
Bandwidth Efficiency and Service Adaptiveness Oriented Data Dissemination in Heterogeneous Vehicular Networks

Heterogeneous network resources are expected to cooperate with each other to support efficient data services in vehicular networks. However, current data scheduling methods cannot efficiently exploit the benefit of heterogeneous wireless communication interfaces in vehicular networks. In this paper, we propose a software-defined network based service architecture, which enables the scheduler to manage heterogeneous network resources in a centralized way. We analyze the heterogeneity of both data items and networks in terms of data sizes and network features (e.g., cost, transmission rate, coverage, etc.), respectively. On this basis, we formulate a data broadcast and network interface selection (DBNIS) problem, which aims to minimize both the service delay and the network access cost. To tackle the problem efficiently, we propose a coding-assisted multiobjective evolutionary algorithm (CMOEA), which consists of two components: packet encoding and network interface selection. Specifically, for packet encoding, we first develop a packet-size based random linear encoding (PRLE) technique to improve bandwidth efficiency. Then, we theoretically analyze the performance bound of PRLE. For network interface selection, we propose a multiobjective algorithm for network interface selection to adaptively satisfy dynamic requirements with respect to service delay and network access cost by deriving a set of pareto-solutions. Finally, we build the simulation model and implement CMOEA for performance evaluation. The comprehensive simulation results demonstrate the superiority of CMOEA under a wide range of scenarios.
Threading and autodocumenting news videos: a promising solution to rapidly browse news topics

This paper describes the techniques in threading and autodocumenting news stories according to topic themes. Initially, we perform story clustering by exploiting the duality between stories and textual-visual concepts through a co-clustering algorithm. The dependency among stories of a topic is tracked by exploring the textual-visual novelty and redundancy of stories. A novel topic structure that chains the dependencies of stories is then presented to facilitate the fast navigation of the news topic. By pruning the peripheral and redundant news stories in the topic structure, a main thread is extracted for autodocumentary