nils-herrmann/clean_scirepeval huggingface dataset:
Effects of device design on InP-based HBT thermal resistance

The thermal resistance of InP-based single and double heterojunction bipolar transistors has been measured. The double heterojunction bipolar transistor (DHBT) device employs an InP collector to improve thermal conductivity and reduce the base-emitter junction temperature rise. DHBTs were grown with heavily doped InGaAs or InP sub-collectors for low resistance contacts. As expected, the all-InP collector (sub-collector and collector) had the lowest thermal resistance while the all-InGaAs collector (sub-collector and collector) had the highest thermal resistance. For a device with emitter size of 1 /spl times/ 3 /spl mu/m/sup 2/, the room temperature thermal resistance of the all-InP collector DHBT was 3.9/spl deg/C/mW. The DHBT with an InGaAs sub-collector had a thermal resistance of 5.6/spl deg/C/mW, while the SHBT had a thermal resistance of 12.3/spl deg/C/mW. Also compared were effects of device layout parameters on thermal resistance and the effect of the topside metal thickness. Devices with the largest perimeter-to-area ratio had the lowest thermal resistance when normalized to emitter area. HBTs with conservative alignment tolerances (L1) had similar thermal resistance to those with aggressive alignment tolerances (L2). The reduced parasitic capacitance of the L2-style SHBT improved the device f/sub T/ from 150 to 183 GHz at 6.0-mA collector current. Alternately, the reduced parasitics allowed the SHBT to operate at 150 GHz f/sub T/ at 2.9 mA, reducing the junction temperature rise by more than half. Doubling the topside metal thickness improved the thermal resistance by 31% at room temperature.
Biasing Samplers to Improve Motion Planning Performance

With the success of randomized sampling-based motion planners such as probabilistic roadmap methods, much work has been done to design new sampling techniques and distributions. To date, there is no sampling technique that outperforms all other techniques for all motion planning problems. Instead, each proposed technique has different strengths and weaknesses. However, little work has been done to combine these techniques to create new distributions. In this paper, we propose to bias one sampling distribution with another such that the resulting distribution out-performs either of its parent distributions. We present a general framework for biasing samplers that is easily extendable to new distributions and can handle an arbitrary number of parent distributions by chaining them together. Our experimental results show that by combining distributions, we can out-perform existing planners. Our results also indicate that not one single distribution combination performs the best in all problems, and we identify which perform better for the specific application domains studied
Comparison of modulation features for phoneme recognition

In this paper, we compare several approaches for the extraction of modulation frequency features from speech signal using a phoneme recognition system. The general framework in these approaches is to decompose the speech signal into a set of sub-bands. Amplitude modulations (AM) in the sub-band signal are used to derive features for automatic speech recognition (ASR). Then, we propose a feature extraction technique which uses autoregressive models (AR) of sub-band Hilbert envelopes in relatively long segments of speech signal. AR models of Hilbert envelopes are derived using frequency domain linear prediction (FDLP). Features are formed by converting the FDLP envelopes into static and dynamic modulation frequency components. In the phoneme recognition experiments using the TIMIT database, the FDLP based modulation frequency features provide significant improvements compared to other techniques (average relative improvement of 7.5 % over the base-line features). Furthermore, a detailed analysis is performed to determine the relative contribution of various processing stages in the proposed technique.
Correspondence Propagation with Weak Priors

For the problem of image registration, the top few reliable correspondences are often relatively easy to obtain, while the overall matching accuracy may fall drastically as the desired correspondence number increases. In this paper, we present an efficient feature matching algorithm to employ sparse reliable correspondence priors for piloting the feature matching process. First, the feature geometric relationship within individual image is encoded as a spatial graph, and the pairwise feature similarity is expressed as a bipartite similarity graph between two feature sets; then the geometric neighborhood of the pairwise assignment is represented by a categorical product graph, along which the reliable correspondences are propagated; and finally a closed-form solution for feature matching is deduced by ensuring the feature geometric coherency as well as pairwise feature agreements. Furthermore, our algorithm is naturally applicable for incorporating manual correspondence priors for semi-supervised feature matching. Extensive experiments on both toy examples and real-world applications demonstrate the superiority of our algorithm over the state-of-the-art feature matching techniques.
Effects of random circuit fabrication errors on the mean and standard deviation of small signal gain and phase of a traveling wave tube

Random fabrication errors in the slow wave circuits may have detrimental effects on the performance of traveling-wave tubes (TWTs) of all types. Pengvanich et. al. considered the evolution of the three forward waves in a TWT in which the Pierce parameters vary randomly along the tube axis. A peculiar feature of the results is that, in the statistical evaluation of a large number of samples with random errors in the circuit phase velocity, a significant number of these samples show an output gain that is higher than the corresponding error-free tube. It was recently proved that the deviation from the mean (which is a second order effect in random errors), is much less than the standard deviation (which is a first order effect in random errors). A significant number of the samples in a statistical analysis would naturally show an output gain that is higher than the corresponding error-free tube. This paper summarizes our recent study, together with an optimization of the random error profile to maximize the small signal gain of a TWT.
An Improved Representation of AC Space-Charge Fields in Steady-State Simulation Codes for Linear-Beam Tubes

Accurate evaluation of the ac space-charge electric field is required in order to obtain accurate predictions for linear-beam-tube performance using steady-state simulation codes. The space-charge field is commonly calculated under the assumption that the beam current density and space-charge fields are all periodic in time and locally periodic in axial distance. In this brief, it is shown by example that there are cases in which it is important, in both the small and large signal regimes, to include departures from this "local spatial periodicity" assumption. We find in the cases that we have studied that it is sufficient to keep a single additional term, proportional to the spatial derivative of the bunched beam current density, in the expression for the space-charge electric field. The resulting expression is much faster to evaluate numerically than a full convolution integral over all axial wavelengths. The improved representation is suitable for use in both small- and large-signal linear-beam simulation codes. It has been implemented and tested in the CHRISTINE large-signal TWT simulation code
Continuation based control of aircraft dynamics

This paper presents an approach to controlling nonlinear aircraft dynamics over a wide range of parameter variation by incorporating bifurcation analysis and numerical continuation methods into the controller design process. This is a significant improvement over the standard control approach of interpolating between a few design points in the flight envelope. Adaptive control in the form of the minimal control synthesis (MCS) is shown to improve the robustness of the controller in the presence of modelling uncertainties. The above methods are applied to a second-order complex nonlinear highly maneuverable aircraft model.
A 3-D Ray Tracing Model for Macrocell Urban Environments and its Verification with Measurements

This paper presents verification results of an advanced 3-D ray tracing model in a macrocell urban environment The verification is based on wide-band double-directional channel measurements at a center frequency of 5.2 GHz and a bandwidth of 120 MHz. For the verification two site-specific measurement routes in the city center of Karlsruhe, Germany are used. Both, the wide-band and the directional prediction of the ray tracing are evaluated. For the verification of the directional prediction the maximum-likelihood channel parameter estimation framework RIMAX is applied to the measurement data. Results show a strong level of agreement of delay spread and azimuth spread. (6 pages)
Applying Pedagogical Approaches to Enhance Learning: Linking Self-Regulated and Skills-Based Learning with Support from Moodle Extensions

This paper provides an overview of the linkage of two pedagogical approaches, Skills-based Learning and Self-Regulated Learning (SRL) supported by software. In linking these approaches, adaptive skills-based learning tools based on a psycho-pedagogical competence model, were assigned to a cyclic SRL process-model. The INNOVRET project provides the underlying framework in which this new model will be tested. Software components for Moodle (the learning management system of choice) were specifically tailored to suit the needs of the project's targeted audience (i.e. heat pump installers). The entire system/model will be critically analysed in this paper.
Deep Image Matting

Image matting is a fundamental computer vision problem and has many applications. Previous algorithms have poor performance when an image has similar foreground and background colors or complicated textures. The main reasons are prior methods 1) only use low-level features and 2) lack high-level context. In this paper, we propose a novel deep learning based algorithm that can tackle both these problems. Our deep model has two parts. The first part is a deep convolutional encoder-decoder network that takes an image and the corresponding trimap as inputs and predict the alpha matte of the image. The second part is a small convolutional network that refines the alpha matte predictions of the first network to have more accurate alpha values and sharper edges. In addition, we also create a large-scale image matting dataset including 49300 training images and 1000 testing images. We evaluate our algorithm on the image matting benchmark, our testing set, and a wide variety of real images. Experimental results clearly demonstrate the superiority of our algorithm over previous methods.
Double-Directional Dual-Polarimetric Cluster-Based Characterization of 70–77 GHz Indoor Channels

This paper presents 70–77 GHz wideband channel characteristics of delay, spatial, and polarimetric domains for small office and entrance hall scenarios. Based on measured multidimensional power spectra of delay, and directions (i.e., azimuth and elevation) of departure and of arrival, multipath clusters were detected using the K-means, threshold-based, and Gaussian-mixture-model clustering algorithms. The spatial positions and directions of clusters, including the first- and last-hop scatterers, are determined using a measurement-based ray tracer method. Our experimental analysis reveals that the composite and cluster-level channel behaviors are considerably dependent on the geometry of environments, the interaction/bouncing order between a wave and objects, the position of transmitter and receiver, the underlying antenna type, and the polarimetric combination. The angular spread of arrival is observed larger than that of departure, and the characteristics for the horizontal polarization are comparable with those for vertical polarization. Our results also manifest the merits of threshold-based clustering algorithm in terms of clusters’ compactness, separation, and exclusiveness. We further find that the identified interaction points exhibit excellent agreement with real physical objects existing in the environment. Based on these results, an indoor stochastic channel model is established not only for the composite parameters and cluster characteristics, but also for the first- and last-hop scatterers’ position, direction, and their dispersive statistics. This model can be used to generate channel realizations of reasonable spatial consistency.
Effect of filler on the microwave dielectric properties of polyethylene/ceramic composites

The present study investigates the effect of various ceramic fillers on the dielectric properties of polyethylene based composites. The fillers used were Sr<inf>9</inf>Ce<inf>2</inf>Ti<inf>12</inf>O<inf>36</inf> (SCT), Ca[(Li⅓Nb⅔)<inf>0.8</inf>Ti<inf>0.2</inf>]O<inf>3-δ</inf> (CLNT) and Sm<inf>2</inf>Si<inf>2</inf>O<inf>7</inf> (SS) with relative permittivities 173, 38 and 12 respectively. The composites were synthesized by melt mixing and hot pressing methods. The effect of different volume fractions of the ceramic (vf = 0 – 0.5) on the dielectric properties of the composites was investigated at 8 GHz. The dielectric properties (relative permittivity and dielectric loss) were found to increase with the ceramic filler content. The relative permittivities obtained experimentally were compared with that of the theoretical predictions.
A Parameterized Stereo Vision Core for FPGAs

We present a parameterized stereo vision core suitable for a wide range of FPGA targets and stereo vision applications. By enabling easy tuning of algorithm parameters, our system allows for rapid exploration of the design space and simpler implementation of high-performance stereo vision systems. This implementation utilizes the Census Transform algorithm to calculate depth information from a pair of images delivered from a simulated stereo camera pair. This work advances our previous work through implementation improvements, a stereo camera pair simulation framework, and a scalable stereo vision core.
Adaptive learning algorithm for SVM applied to feature tracking

The framework of support vector machines (SVM) is becoming extremely popular in the field of statistical pattern classification. In this paper we investigate a technique which couples Kalman filter closely with the SVM. The problem of object tracking can be seen as a pattern recognition problem. However, because of the dynamics, this pattern might experience some changes over time. In order to keep track of the position of the pattern and to make out the desired pattern from the background, we must have some strong continuous time model. We propose an algorithm which combines the Markov property of the Kalman filter with the strong classification capability of SVM. The whole system has been tested on real life problems and we found that with this framework we could track a particular object even in a frame which contains identical objects. The results were compared to that of obtained by color blob tracking which showed the strength of the approach.
Epitaxial SiGeC/Si photodetector with response in the 1.3-1.55 /spl mu/m wavelength range

We demonstrate a Si-based photodetector with a response in the 1.3-1.55 /spl mu/m wavelength range. The active absorption layer of the pin photodiode consists of a pseudomorphic SiGeC alloy grown on a Si substrate with a Ge content of 55% and a thickness of 800 /spl Aring/. By using a single-mode fiber butt coupled to the waveguide facet, the external quantum efficiency for a 400-/spl mu/m long waveguide is 0.2% at 1.55 /spl mu/m, and 8% at 1.3 /spl mu/m. The external efficiency can be further improved by using a multiple layer absorber structure. The high efficiency and high speed characteristics together with the low leakage current density imply potential application of the SiGeC/Si photodetector for optical fiber communications and optical interconnects in the 1.3-1.55 /spl mu/m wavelength range.
Audio events detection based highlights extraction from baseball, golf and soccer games in a unified framework

We have developed a unified framework to extract highlights from three sports - baseball, golf and soccer - by detecting some of the common audio events that are directly indicative of highlights. We used MPEG-7 audio features and entropic prior hidden Markov models (HMM) for feature extraction and classification, respectively, to recognize these common audio events. Together with pre- and post-processing techniques using general sports knowledge, we have been able to generate promising results dealing with an audio track that is dominated by audio mixtures and noisy background.
Device design considerations for ultra-thin SOI MOSFETs

The ultra-thin SOI (UTSOI) device is an attractive choice for sub-10 nm gate-length scaling. In this work the major issues for UTSOI are addressed. External resistance is minimized by using the raised extension (REX) process flow which features an offset spacer to minimize the region of UTSOI outside the channel. The REX process scheme is used to demonstrate improved pFET performance and also to demonstrate the first planar single gate nFET with 8 nm gate-length. High temperature mobility measurements show that the channel thickness can be scaled further than previously predicted. UTSOI devices with tungsten gates and HfO/sub 2/ gate dielectrics having appropriate threshold voltages are presented for the first time.
Application of finite element methods to photonic crystal modelling

Photonic crystals are materials with periodically modulated dielectric constant, through which certain frequencies of electromagnetic radiation cannot propagate; the luminary analogues of semiconductors. The modes admitted by photonic crystals can be investigated effectively using the finite element method with the assistance of the Bloch-Floquet theorem, by considering a unit cell of the material and imposing periodic boundary conditions. Along with the Dirichlet and metric matrices, a third type of elemental matrix emerges. The types of results that are of interest to photonic crystal manufacturers are introduced and presented; in this context, the benefits of using subspace iteration techniques to solve the eigensystems are discussed.
An interference nulling algorithm for a single receiver phased array

An adaptive technique utilizing phase-only in interference suppression for a planar phased array is presented. No a priori knowledge of the interference direction is assumed, and the observed data are at the array output only: a single-receiver system. The sequential orthogonal search directions in the phase space are predetermined, and the algorithm is very simple computationally. Using the criterion of minimizing the interference power and a soft constraint in the look direction, for a 1369 element planar array and three interference sources, the required phase distribution is reached after approximately 220 time samples, or 11 ms (sampling rate 200 kHz) The convergence time varies approximately as a linear function of the number of interference sources, and is essentially independent of the array size. Some computed results are given.
Complete structure and motion from two monocular sequences without stereo correspondence

It is demonstrated that the motion and structure of rigidly moving objects can be completely determined from two monocular image sequences using only temporal matches. Three aspects of this scheme are useful: since stereo matching is not necessary, two cameras can view totally different parts of the rigid scene; as temporal disparity is usually significantly smaller than stereo disparity, matching needs only to deal with relatively small disparities; the recoverable scene structure is defined by the union of the fields of view of two cameras instead of the intersection, and so is much larger than that of a conventional stereo setup. Experiments with synthesized data and real world images are presented to demonstrate the feasibility of this scheme.<<ETX>>
A new scheme for extracting multi-temporal sequence patterns

This paper proposes a new scheme for unsupervised multi-temporal sequence pattern extraction. The main idea of the scheme is iterative coarse to fine data examination. We decompose a pattern into ambiguous subpatterns and distinguishable sub-patterns (DSP). In each iteration, we coarsely examine the training temporal signal sequence by training an Elman neural network. The trained Elman network is used to select the DSP candidate set. Then, we look at the training signals around the DSPs and use maximum likelihood criteria to expand them into whole patterns. We cut out the new found patterns from the training signal sequence and repeat the whole procedure until no more new patterns are found. The experimental result shows this method promising.
Effects of beam velocity spread on the excitation of backward waves in beam tunnels of high-power gyrotrons

The excitation of backward waves in the beam tunnel of a high power gyrotron is considered. Such instability is believed to degrade beam quality and consequently gyrotron efficiency. The instability occurs in a region of the beam tunnel where both the magnetic field and beam tunnel radius are strong functions of axial position. The instability can be suppressed by coating the walls of the beam tunnel. Here we consider the effect of velocity spread and the coupling to electrostatic cyclotron waves on the conditions for BWO excitation. Results will be reported for beam tunnel parameters relevant to 170 GHz, MW class gyrotron for plasma heating.
A data driven method for feature transformation

Most image understanding algorithms begin with the extraction of information thought to be relevant to the particular task. This is commonly known as feature extraction and has, up to this date, been a largely manual process, where a reasonable method is chosen through validation on the experimented dataset. In this work we propose a data driven, local histogram based feature extraction method that reduces the manual intervention during the feature computation process and improves on the performance of widely used gradient histogram based features (e.g., HOG). We demonstrate favorable object detection results against HOG on the Inria Pedestrian[7], Pascal 2007[10] data.
A statistical approach for estimating the loss contribution of concatenated connectors in fiber-optic links

It is shown that the elementary approach to the calculation of concatenated fiber-optic connector loss of simply adding best-case and worst-case losses leads to an unrealistic minimum and maximum link loss prediction. A more accurate statistical approach based on the loss distribution of a single connector with a method for numerical convolution is developed. For a 10-connector link, the statistical model predicts the 1% and 99% loss values as 5.52 dB and 8.75 dB, respectively, based on available data for a particular connector used in military fiber-optic systems. By simply multiplying the single connector 1% and 99% loss values by ten, a connector loss range of 3.2 dB to 14.8 dB is predicted. Appropriate calculation of concatenated connector loss values can lead to relaxation of the performance requirements of the remaining components. This method is easily expanded to include all other components that contribute to link budget calculations.<<ETX>>