nils-herrmann/clean_scirepeval huggingface dataset:
Collisionless self-focusing of CO 2 10 µm P(20) laser light in SF 6

We develop a simple intuitive picture of the vibration-rotation structure of the SF 6 molecule such that the molecular susceptibility responsible for self-focusing can be calculated. We treat the propagation dynamics by generalizing the standard steady-state Gaussian propagation equations to include the important effect of absorption in the wings of the spatial profile. By calibrating the model to absorption data at CO 2 10 μm P(2) we find good agreement with beam waist data at the same wavelength. Absorption in the wings is dominant at low laser fluences, and the real part of the susceptibility is responsible for the defocusing-to-focusing turnover in the beam waist near 100 mJ/cm2, consistent with the interpretation of Nowak and Ham .
Design of optimum power, delay efficient level shifter for biomedical applications

The need for low power and less delay CMOS technology has led to the emergence of multi-voltage domains for biomedical applications. So to interface with these domains, a level shifter forms an essential component. Also a level shifter reduces the crowbar current that arises due to different voltage domains. This paper focusses on less delay and an optimum power dissipated level shifter which has additional keeper transistors compared to an earlier proposed level shifter. The keeper transistors reduce the delay in the regeneration latch by switching ON the corresponding PMOS transistors whenever the output node is low. Thus the proposed level shifter could efficiently convert from 1.1V to 1.8V with an optimum power dissipation of 33.52ps and a delay of 103.68ps for a supply voltage of 1.8V. Also, it could tolerate supply voltages of 18V and provide a shift from 3V to 18V efficiently. The simulations are done under 180nm CMOS technology tool in the Mentor Graphics.
Deterministic load flow algorithm for balanced radial AC distribution systems

The developed load flow algorithm is computationally efficient, provides a good convergence property, is independent of load model, nos. of laterals and sub-laterals, nos. of buses and R/X ratio of conductors, can be used for online as well as offline applications, and in contingency. Loads, in the present formulation, have been represented as constant power. Finally, IEEE 69-bus system is used as a test system to demonstrate the effectiveness of the proposed work. The load flow results and results for critical loading condition for constant complex power load obtained for 69-bus system have been verified with the results published with other existing method and found to be in exact agreement. The convergence characteristics is analysed for various system parameters and loading conditions. The proposed distributed load flow method is superior in terms of time taken for execution when compared with six other existing methods for 33-node as well as 69-node network.
A note on correlation distance of vhf fading from irregularities in the equatorial ionosphere

Tests using satellite signals at 136 MHz have been conducted at NASA STADAN (Satellite Tracking and Data Acquisition Network) in Santiago, Chile, and the spatial correlation coefficient for an equatorial ionospheric disturbance has been computed. Test data were obtained on telemetry antennas separated from east to west by 1168 feet. The polarization correlation of the signal (horizontal and vertical) at one antenna is shown to be high (0.68) The correlation for the 1168-foot spacing is shown to be low (0.2). Other data from Minitrack interferometer measurements are also included.
Basis pursuit for tracking

This paper introduces a novel adaptive texture feature selection algorithm for tracking. Specifically, we provide a statistical wavelet basis paradigm to maximally separate statistical characteristics of the object-in-interest and its background. The algorithm is based upon nonlinearly selecting basis elements out of dual dictionaries in an iterative fashion to continually improve a cost function that is suitable for tracking. We demonstrate that such a selection is effective with several difficult sequences that are affected by lighting changes, occlusion and background motion.
Bilevel sparse coding for coupled feature spaces

In this paper, we propose a bilevel sparse coding model for coupled feature spaces, where we aim to learn dictionaries for sparse modeling in both spaces while enforcing some desired relationships between the two signal spaces. We first present our new general sparse coding model that relates signals from the two spaces by their sparse representations and the corresponding dictionaries. The learning algorithm is formulated as a generic bilevel optimization problem, which is solved by a projected first-order stochastic gradient descent algorithm. This general sparse coding model can be applied to many specific applications involving coupled feature spaces in computer vision and signal processing. In this work, we tailor our general model to learning dictionaries for compressive sensing recovery and single image super-resolution to demonstrate its effectiveness. In both cases, the new sparse coding model remarkably outperforms previous approaches in terms of recovery accuracy.
Development and optimisation of a zinc oxide nanogenerator

A nanogenerator is developed using zinc oxide (ZnO) nanowires. Design of experiments is used to optimise the growth parameters of the ZnO nanowires, with an increase in output voltage set as the optimising parameter. After the optimisation of the nanowire growth, focus is shifted to the optimisation of the manufacturing of the nanogenerator. The nanowires are plasma treated and annealed right after growth, both methods increasing the output voltage. The last step in manufacturing is adding PMMA for stability and durability. Various materials are dispersed in the PMMA to further enhance the output voltage. Single walled carbon nanotubes and ZnO nanowires gave the biggest increase in output voltage. A nanogenerator manufactured with a combination of these optimisation techniques resulted in an output voltage of over 5 V, a ten-fold increase of output voltage.
Discriminative learning of visual words for 3D human pose estimation

This paper addresses the problem of recovering 3D human pose from a single monocular image, using a discriminative bag-of-words approach. In previous work, the visual words are learned by unsupervised clustering algorithms. They capture the most common patterns and are good features for coarse-grain recognition tasks like object classification. But for those tasks which deal with subtle differences such as pose estimation, such representation may lack the needed discriminative power. In this paper, we propose to jointly learn the visual words and the pose regressors in a supervised manner. More specifically, we learn an individual distance metric for each visual word to optimize the pose estimation performance. The learned metrics rescale the visual words to suppress unimportant dimensions such as those corresponding to background. Another contribution is that we design an appearance and position context (APC) local descriptor that achieves both selectivity and invariance while requiring no background subtraction. We test our approach on both a quasi-synthetic dataset and a real dataset (HumanEva) to verify its effectiveness. Our approach also achieves fast computational speed thanks to the integral histograms used in APC descriptor extraction and fast inference of pose regressors.
Algebraic signatures for scalable distributed data structures

Signatures detect changes to data objects. Numerous schemes are in use, especially the cryptographically secure standards SHA-1. We propose a novel signature scheme which we call algebraic signatures. The scheme uses the Galois field calculations. Its major property is the sure detection of any changes up to a parameterized size. More precisely, we detect for sure any changes that do not exceed n-symbols for an n-symbol algebraic signature. This property is new for any known signature scheme. For larger changes, the collision probability is typically negligible, as for the other known schemes. We apply the algebraic signatures to the scalable distributed data structures (SDDS). We filter at the SDDS client node the updates that do not actually change the records. We also manage the concurrent updates to data stored in the SDDS RAM buckets at the server nodes. We further use the scheme for the fast disk backup of these buckets. We sign our objects with 4-byte signatures, instead of 20-byte standard SHA-1 signatures. Our algebraic calculus is then also about twice as fast.
Automatic Detection of Arrhythmias Using Wavelets and Self-Organized Artificial Neural Networks

The arrhythmias or abnormal rhythms of the heart are common cardiac riots and may cause serious risks to the life of people, being one of the main causes on deaths. These deaths could be avoided if a previous monitoring of these arrhythmias were carried out, using the Electrocardiogram (ECG) exam. The continuous monitoring and the automatic detection of arrhythmias of the heart may help specialists to perform a faster diagnostic. The main contribution of this work is to show that self-organized artificial neural networks (ANNs), as the ART2, can be applied in arrhythmias automatic detection, working with Wavelet transforms for feature extraction. The self-organized ANN allows, at any time, the inclusion of other groups of arrhythmias, without the need of a new complete training phase. The paper presents the results of practical experimentations.
A Loopy Belief Propagation approach for robust background estimation

Background estimation, i.e. automatic recovery of the background image from a sequence of images containing moving foreground objects, is an important module in many applications, e.g. surveillance and video segmentation. In this paper, we present a simple, yet effective and robust approach for background estimation based on loopy belief propagation. Robustness of the proposed approach means: (i) minimal assumption on the input frames, and (ii) no need to tune parameters. Basically, the background can be recovered even when the occluding foreground objects stay still for a long time. Furthermore, no motion information needs to be known or estimated for the foreground objects, which implies that background can be recovered from a set of frames which are not consecutive temporally. Analysis and experiments are provided to compare the proposed approach to related methods. Experimental results on typical surveillance videos demonstrate the effectiveness of our approach.
A quantitative statistical analysis of in-situ IC and electronics in complex and wave-chaotic enclosures

The objective of this work is to investigate a hybrid deterministic and stochastic formulation for a quantitative statistical analysis of in-situ IC and electronics in complex and wave-chaotic enclosures. The key technical ingredients include: (ii) a stochastic dyadic Green's function method for wave interaction with wave-chaotic media, which quantitatively describes the universal statistical property of chaotic systems through random matrix theory; (ii) a hybrid deterministic and stochastic formulation based on the optimized multi-trace integral equation domain decomposition method, which enables a statistical prediction of in-situ IC and electronics in complex wave-chaotic environments. The capability and benefits of the computational algorithms will be exploited, illustrated and validated through a variety of 3D product-level IC and electronic systems.
An Energy Minimization Approach for Automatic Video Shot and Scene Boundary Detection

Shot and scene boundary detection is a primary and an important step towards all the video condensation techniques. In this paper a simple but automatic and efficient scene and shot boundary detection is proposed based on energy minimization approach. A shot differs in its background from another. The background has a temporal continuity throughout the shot, we have invoked this fact in our algorithm. We formulate the extraction of Video shot as an energy minimization problem in which the desire to extract the shot with the common background is represented by an energy term. The energy minimized approach optimizes to minimize the discontinuity in the background edges and color. We have proposed different algorithms for different kinds of shot transitions(cut, fade-in, fade-out, dissolve and wipe) to help us to get a better solution for shot boundary detection problem. We have demonstrated our method with a variety of videos which is capable of accurately detecting shot transitions.
0.5 THz Performance of a Type-II DHBT With a Doping-Graded and Constant-Composition GaAsSb Base

A type-II GaAsSb Double Heterojunction Bipolar Transistor (DHBT) with an AlInP emitter, doping-graded GaAsSb base, and InP collector has been grown by molecular beam epitaxy. The 0.38 μm emitter devices have been fabricated by a triple-mesa wet etch process and demonstrated f<sub>T</sub>/f<sub>MAX</sub>=470/540 GHz at J<sub>C</sub>=5.1 mA/μm<sup>2</sup> and V<sub>CB</sub>=0.65 V. This performance is comparable with composition-graded base devices with similar emitter width.
60 GHz-Ultrawideband Real-Time Multi-Antenna Channel Sounding for Multi Giga-Bit/s Access

60 GHz technology has been proposed recently for multi-Giga-bit/s access within different application scenarios, e.g. in crowded multi-user public transportation (PT) environments. Hence, channel sounders that are used to investigate multipath propagation and multi-user access techniques including spatial diversity and beam-forming in time-variant shadowing environments require ultra-wideband (UWB) operation, multiple antennas, and real-time. This paper presents probably the first channel sounder architecture fulfilling these requirements. Moreover, an experimental study is described which is performed for planning purposes and deployment of 60 GHz WLAN/WPAN networks in PT scenarios. Coverage analysis based on different PHY channelization schemes, including multiple access point configurations and antennas is addressed for the Airbus 340 cabin. Additionally, distributions of time dispersion, coherent bandwidth, and Rice K-factor in the in-cabin coverage area are presented. Specifically, a brief analysis under dynamic channel conditions due to human activities is introduced. The suitability of the 60 GHz-UWB channels sounder for PT scenarios is demonstrated. Besides, some channel results for finding the most suitable system parameters for 60 GHz access deployment of in-flight-entertainment (IFE) systems are given.
Design considerations for accelerated life tests conducted on cable hardware subject to cathodic delamination

The accelerated life test (ALT) is a tool used by cable manufacturers and cable end-users to determine how long a given piece of hardware will last once deployed into the marine environment. To conduct an ALT, the failure mechanism for the hardware in question must be known, and the kinetics of that process must be determined via experimentation. Typically, Arrhenius kinetics are assumed and the activation energy for the dominant degradation process must be measured. Cathodic delamination is a major failure mechanism for undersea cable connectors. Although the mechanism via which cathodic delamination weakens/destroys metal-polymer bonds is well known, good quality kinetic data for the process (from which Arrhenius activation energies can be derived) is generally lacking. Our research into the cathodic debonding process has revealed that there are three major cases that must be examined separately to obtain the kinetic parameters needed to make the time-temperature conversions for an ALT. Case (1) concerns thin polymer films (e.g., paints) applied directly over, and completely covering, a cathodically-polarized metal surface. Here, the water and oxygen needed for the cathodic reduction of dissolved oxygen reaction on the metal surface can diffuse directly and rapidly through the polymer layer. Two different methods for calculating the diffusivity of water through the polymer layer will be discussed. Once water diffusivity at three different temperatures has been measured, the natural logarithm of the diffusivity values can be plotted versus the reciprocal absolute temperature to obtain the slope of a line from which the Arrhenius activation energy for water diffusion through the polymer can be calculated. Cases (2) and (3) involve macroscopically thick layers of polymer with exposed polymer/metal bond lines. If metal-polymer bonds susceptible to attack by cathodic delamination are present, debonding will proceed inwards from the exposed edges. Measurements of the rate at which the width of the debonded region grows at three different temperatures can be used to calculate an Arrhenius activation energy. Case (3) is more difficult because it concerns materials/coatings that are resistant to cathodic delamination; no obvious debonding occurs during the early part of the ALT. For this case, a reaction acceleration factor (RAF) must be experimentally determined by measuring the corrosion current density (icorr) for the reduction of water reaction using the two metals involved (usually a sacrificial anode and the metal surface of interest). The icorr value for the sacrificial anode/metal substrate pair is measured using a potentiostat at the anticipated service temperature and also at the intended ALT temperature. The ratio of these two values is the RAF and can be used directly to determine time-temperature conversion factor for the ALT.
Design of a frequency tripling, third harmonic gyro-TWT

A two-stage gyro-traveling-wave-tube(gyro-TWT) separated by a radiation-free drift section is being designed. The rf input and output sections of gyro-TWT operate at 11.5 GHz fundamental harmonic and 34.5 GHz third harmonic respectively. By choosing a proper drift section length, the third harmonic of the modulated electron beam current at the entrance to the output waveguide may be comparable to the fundamental harmonic due to the ballistic bunching of the beam in the drift section. The well prebunched beam makes beam-wave interaction in the output waveguide more efficient. The magnetic field B/sub 0/ is about 4.4 kG so that permanent magnetic packaging is possible. This type gyro-TWT features subharmonic injection, compact volume and superior stability.
A Fast 2D Shape Recovery Approach by Fusing Features and Appearance

In this paper, we present a fusion approach to solve the nonrigid shape recovery problem, which takes advantage of both the appearance information and the local features. We have two major contributions. First, we propose a novel progressive finite Newton optimization scheme for the feature-based nonrigid surface detection problem, which is reduced to only solving a set of linear equations. The key is to formulate the nonrigid surface detection as an unconstrained quadratic optimization problem that has a closed-form solution for a given set of observations. Second, we propose a deformable Lucas-Kanade algorithm that triangulates the template image into small patches and constrains the deformation through the second-order derivatives of the mesh vertices. We formulate it into a sparse regularized least squares problem, which is able to reduce the computational cost and the memory requirement. The inverse compositional algorithm is applied to efficiently solve the optimization problem. We have conducted extensive experiments for performance evaluation on various environments, whose promising results show that the proposed algorithm is both efficient and effective.
Application of a parasitic aware model to optimize an RF energy scavenging circuit fabricated in 130 nm CMOS

Design parameters, including transistor width and number of stacked stages, contribute to the efficiency of RF scavenging systems. This leads to a large design space and, as a result, designing optimal RF scavenging circuits for a given performance requirement is a difficult problem. This work presents an analytical model based on the physical design parameters of the power matched Villard voltage doubler. This model is successfully used to determine the optimal design of an RF energy scavenging circuit fabricated in a 130 nm IBM process.
BioWatch — A wrist watch based signal acquisition system for physiological signals including blood pressure

A wrist watch based system, which can measure electrocardiogram (ECG) and photoplethysmogram (PPG), is presented in this work. By using both ECG and PPG we also measure pulse transit time (PTT), which studies show to correlate well with blood pressure (BP). The system is also capable of monitoring heart rate using either ECG or PPG and can monitor blood oxygenation by easily replacing the PPG sensors with a different set. In this work, we investigate methods to train a fitting function to convert a PTT measurement to its corresponding systolic BP. We also validate measurements on different postures and show the value of calibrating the device for each posture. This system, called BioWatch, can potentially facilitate continuous and ubiquitous monitoring of ECG, PPG, heart rate, blood oxygenation and BP.
Discriminant simplex analysis

Image representation and distance metric are both significant for learning-based visual classification. This paper presents the concept of k-nearest-neighbor simplex (kNNS), which is a simplex with the vertices as the k nearest neighbors of a certain point. kNNS contributes to the image classification problem in two aspects. First, a novel distance metric between a point to its kNNS within a certain class is provided for general classification problem. Second, we develop a new subspace learning algorithm, called discriminant simplex analysis (DSA), to pursue effective feature representation for image classification. In DSA, the within-locality and between-locality are both modeled by kNNS distance, which provides a more accurate and robust measurement of the probability of a point belonging to a certain class. Experiments on real-world image classification demonstrate the effectiveness of both DSA as well as kNNS based classification approach.
A Parameter-Free Framework for General Supervised Subspace Learning

Supervised subspace learning techniques have been extensively studied in biometrics literature; however, there is little work dedicated to: 1) how to automatically determine the subspace dimension in the context of supervised learning, and 2) how to explicitly guarantee the classification performance on a training set. In this paper, by following our previous work on unified subspace learning framework in our earlier work, we present a general framework, called parameter-free graph embedding (PFGE) to solve the above two problems by posing a general supervised subspace learning task as a semidefinite programming problem. The semipositive feature Gram matrix, namely the product of the transformation matrix and its transpose, is derived by optimizing a trace difference form of an objective function extended from that in our earlier work with the constraints that guarantee the class homogeneity within the neighborhood of each datum. Then, the subspace dimension and the feature weights are simultaneously obtained via the singular value decomposition of the feature Gram matrix. In addition, to alleviate the computational complexity, the Kronecker product approximation of the feature Gram matrix is proposed by taking advantage of the essential matrix form of image pixels. The experiments on simulated data and real-world data demonstrate the capability of the new PFGE framework in estimating the subspace dimension for supervised learning as well as the superiority in classification performance over traditional algorithms for subspace learning