nils-herrmann/clean_scirepeval huggingface dataset:
Cogging Torque in Flux-Switching Permanent Magnet Machines

The influence of manufacturing tolerances on the cogging torque waveforms, as well as the back-EMF waveforms, in 3- and 5-phase flux-switching permanent magnet (FSPM) machines having different numbers of stator/rotor poles is investigated by finite element analyses and experiments. The cause of excessive cogging torque in the prototype FSPM machines has been identified. It is due to modular stator structure which makes it mechanically weak, leading to the intrusion of C-core into the airgap and an uneven inner stator bore. The analyses presented in the paper should be useful to the design and analysis of modular PM machines in general.
Algorithm-Based Fault Tolerance Applied to P2P Computing Networks

P2P computing platforms are subject to a wide range of attacks. In this paper, we propose a generalisation of the previous disk-less checkpointing approach for fault-tolerance in High Performance Computing systems. Our contribution is in two directions: first, instead of restricting to 2D checksums that tolerate only a small number of node failures, we propose to base disk-less checkpointing on linear codes to tolerate potentially a large number of faults. Then, we compare and analyse the use of Low Density Parity Check (LDPC) to classical Reed-Solomon (RS) codes with respect to different fault models to fit P2P systems. Our LDPC disk-less checkpointing method is well suited when only node disconnections are considered, but cannot deal with byzantine peers. Our RS disk-less checkpointing method tolerates such byzantine errors, but is restricted to exact finite field computations.
BioWatch: A Noninvasive Wrist-Based Blood Pressure Monitor That Incorporates Training Techniques for Posture and Subject Variability

Noninvasive continuous blood pressure (BP) monitoring is not yet practically available for daily use. Challenges include making the system easily wearable, reducing noise level and improving accuracy. Variations in each person's physical characteristics, as well as the possibility of different postures, increase the complexity of continuous BP monitoring, especially outside the hospital. This study attempts to provide an easily wearable solution and proposes training to specific posture and individual for further improving accuracy. The wrist watch-based system we developed can measure electrocardiogram and photoplethysmogram. From these two signals, we measure pulse transit time through which we can obtain systolic and diastolic blood pressure through regression techniques. In this study, we investigate various functions to perform the training to obtain blood pressure. We validate measurements on different postures and subjects, and show the value of training the device to each posture and each subject. We observed that the average RMSE between the measured actual systolic BP and calculated systolic BP is between 7.83 to 9.37 mmHg across 11 subjects. The corresponding range of error for diastolic BP is 5.77 to 6.90 mmHg. The system can also automatically detect the arm position of the user using an accelerometer with an average accuracy of 98%, to make sure that the sensor is kept at the proper height. This system, called BioWatch, can potentially be a unified solution for heart rate, SPO2 and continuous BP monitoring.
ARC24 — A double-arch positioner for bistatic RCS measurements with four degrees of freedom

Bistatic RCS measurements are complex and time-demanding due to the different measurement dimensions. In cases, where target characterization requires a large amount of bistatic angle pairs, automated measurement facilities are demanded, that cover large spatial dimensions. This paper presents a positioning system for overcoming some of the typical limitations and makes the procedure of measuring bistatic RCS measurements more automated and cost-effective.
Architecture and implementation of real-time 3D stereo vision on a Xilinx FPGA

Many applications from autonomous vehicles to surveillance can benefit from real-time 3D stereo vision. In the present work we describe a 3D stereo vision design and its implementation that exploits effectively the resources of a Xilinx Virtex-5 FPGA. The post place-and-route design achieved a processing rate of 87 frames per sec (fps) for 1920 × 1200 resolution. The hardware prototype system was tested and validated for several data sets with resolutions up to 400 × 320 and we achieved a processing rate of 1570 fps.
Electromagnetic properties of corrugated and smooth waveguides filled with radially inhomogeneous plasma

Creation and diagnosis of plasma in microwave devices remains one of the primary challenges of plasma microwave electronics. In the present work we deal with (1) diagnostic techniques for characterization of radially nonuniform plasma columns suitable for use in high-power microwave sources and (2) the effects of such plasmas on the electromagnetic properties of finite length, spatially periodic slow wave structures. Experimental studies were performed both for a strong and a weak guiding magnetic field. Using a combination of a microwave resonator (X-band) and a Langmuir probe, both the plasma peak density and its transverse profile were measured in situ (prior to beam propagation). The frequency upshifts of the TM/sub 01n/ modes in an open corrugated cavity were measured as a function of the background plasma density. The dispersion diagrams were reconstructed, for the first time, up to a peak plasma density of 10/sup 12/ cm/sup -3/. Frequency upshifts and "flattening" of the dispersion curves were observed for both strong and weak guiding magnetic fields. For plasma density above 5/spl times/10/sup 11/ cm/sup -3/ the frequency upshifts are less than anticipated by theory.
A Syntactic-Semantic Approach to Image Understanding and Creation

A syntactic-semantic approach to information extraction from images is described. The methodology involves the injection of semantic considerations into context-free grammars. The semantic considerations include feature vectors, selection restrictions, feature transfer functions, semantic well-formedness, etc. With such injection, we can make a description scheme which carries the numerical, the structural, and the a prior real world knowledge about the pattern we want to extract. From the description we can construct an analytical mechanism, the creation machine, which wil find the desired pattern amid a chaos of noisy primitives.
An experimental study of coupled hidden Markov models

In this work, ail experimental study of the coupled hidden Markov models (CHMMs) is carried out. CHMMs are directed graphical models of stochastic processes and are a special type of Dynamic Bayesian Networks (DBNs). The DBNs generalize the hidden Markov models by representing the hidden states as state variables, and allow the states to have complex interdependencies. This study considers the probabilistic inference problem and the learning problem of these models. A series of experiments were carried out to evaluate the relationship between the learning outcome and various factors in the learning process. In addition, this study looks into the capabilities of the CHMMs in the context of Maximum Likelihood classification of temporal patterns. Empirical results suggest that even with perfect learning, the classification error can be significant in some cases, and it is important to limit the state space of the models when considering the framework in real-world applications.
A novel antenna concept for double-directional channel measurements

In this paper, a novel concept for the characterization of the double-directional mobile radio channel is presented. It is based on a broad-band real-time channel-sounding device together with appropriate antenna arrays, thus forming a multiple-input-multiple-output system. For the propagation measurements within the 5.2-GHz wireless local area network band presented here, a uniform linear antenna array at the receiver site and a switched multibeam antenna at the transmitter site have been employed. By application of the multibeam antenna, the entire azimuthal range of 360/spl deg/ is covered without ambiguities. The measurement data obtained with this platform in a simple synthetic scenario, as well as in a typical multipath environment, is then analyzed in terms of the propagation path properties. Using the three-dimensional estimation of signal parameters via rotational invariance techniques algorithm together with a proper Fourier transform, the channel parameters of all relevant multipaths such as delay, direction of arrival, direction of departure, and path loss are extracted from the measurement data. For both scenarios, the obtained estimated paths are verified to the underlying physical propagation environment by means of geometrical considerations. This geometrical matching accounts for multipath components due to single-bounce as well as multiple-bounce reflections and demonstrates the high suitability and good performance of the proposed concept.
Action recognition using micro-Doppler signatures and a recurrent neural network

This paper explores the long short-term memory (LSTM) recurrent neural network for human action recognition from micro-Doppler signatures. The recurrent neural network model is evaluated using the Johns Hopkins MultiModal Action (JHUMMA) dataset. In testing we use only the active acoustic micro-Doppler signatures. We compare classification performed using hidden Markov model (HMM) systems trained on both micro-Doppler sensor and Kinect data with LSTM classification trained only on the micro-Doppler signatures. For HMM systems we evaluate the performance of product of expert based systems and systems trained on concatenated sensor data. By testing with leave one user out (LOUO) cross-validation we verify the ability of these systems to generalize to new users. We find that LSTM systems trained only on micro-Doppler signatures outperform the other models evaluated.
Classifiers for Motion

In this paper, we present a supervised learning based approach for sub-pixel motion estimation. The novelty of this work is the learning based method itself which tries to learn the shifts from a large training database. Integer pixel shift is sub-divided and discretized to levels in both the horizontal and vertical direction. We pose the problem of motion estimation in a polar coordinate system. Shift estimation in the x and y direction has been posed as a problem of estimating r and thetas. The ordinal property of r has been used, and consequently, we employ a ranking based approach for estimating r. For thetas estimation we employ multi-class classification techniques. We demonstrate how very simplistic features can be used to differentiate between different sub-pixel shifts
A novel approach for stabilizing recursive least squares filters

A novel approach for stabilizing recursive least squares (RLS) filters is presented. The approach relies on a detailed fixed point analysis, which provides two important benefits. The analysis reveals a bias in the error propagation mechanism, providing an analytical basis for instability problems. The analysis then indicates which specific roundoff errors are causing instability. These roundoff errors are then biased in such a way that the overall filter is biased towards stable performance. Experimental results indicate that stability can be achieved with negligible loss in least squares performance.
Discovering recurrent events in video using unsupervised methods

Production videos such as news, sports and movies have a definitive structure that involves short term interaction as well as long term correlation. This structure in video can be captured by models that take into consideration the short term statistics as well as long term recurrence. We investigate the application of probabilistic models that capture this structure. The novel approach is to characterize the short term events in video by models that can account for temporal support in terms of piecewise stationary signals with transitions, These short term events can then be embedded within another temporal model that accounts for transitions between these event and thus characterizes long term history. This also leads to the detection of recurring events in video using a monolithic model. The proposed approach is an unsupervised algorithm for event detection and it can be used for summarization, similarity based matching and enhanced browsing.
Ceres flight model 5 on NPP: Post-launch performance and initial sensor data record validation results

The Clouds and Earth Radiant Energy System (CERES) program has the objective of developing a Climate Data Record CDR of Earth Radiation Budget measurements. CERES Flight Model 5 was placed in orbit in October 2011 aboard the NPP spacecraft. FM-5 joined the FM-1 and FM-2 aboard the Terra and FM-3 and -4 aboard the Aqua spacecraft to continue the Earth radiation budget CDR. This sensor had been calibrated extensively on the ground. Operations in orbit began with calibrations and validation checks. These results agreed with ground calibrations within 1%. Operations to get data with which to compare FM-5 with FM-1 through -4 and with other radiation budget instruments have begun. These comparisons are necessary to assure a seamless Earth radiation budget CDR.
A novel Gaussianized vector representation for natural scene categorization

This paper presents a novel Gaussianized vector representation for scene images by an unsupervised approach. First, each image is encoded as an ensemble of orderless bag of features, and then a global Gaussian Mixture Model (GMM) learned from all images is used to randomly distribute each feature into one Gaussian component by a multinomial trial. The parameters of the multinomial distribution are defined by the posteriors of the feature on all the Gaussian components. Finally, the normalized means of the features distributed in every Gaussian component are concatenated to form a supervector, which is a compact representation for each scene image. We prove that these super-vectors observe the standard normal distribution. Our experiments on scene categorization tasks using this vector representation show significantly improved performance compared with the bag-of-features representation.
Adaptive Transmission With Single-Carrier Multilevel BICM

In this paper, we introduce adaptive link control techniques for multiple-input multiple-output (MIMO) systems with broadband single-carrier signaling. Soft cancellation and minimum mean squared error turbo equalization is assumed, where matching between coding and equalization plays crucial roles to achieve high throughput. This paper uses multilevel coded bit-interleaved coded modulation (ML-BICM) with linear mapping as a core part of the transmission, on top of which this paper applies automatic repeat request (ARQ) with adaptive coding (AC) for link control. The reason behind the use of the linear mapping ML-BICM is the separability between the layers in the modulation format: This should bring us a significant benefit in designing the link control strategies because each layer has its own reliability, and the adaptive link control should be optimized based on the each layer's reliabilities. For ARQ with ML-BICM, we introduce layer-by-layer retransmission control, where turbo equalization and retransmission control can be performed independently over the layers because of the layer-separability. The layer-by-layer concept is then applied to AC with ML-BICM, where the code parameters such as code rate and generator polynomials are chosen so that after several iterations the mutual information between the transmitted and the soft-input soft-output decoder output information can reach a value very close to one while minimizing the rate loss due to the mismatch between the equalization and decoder. Extrinsic information transfer analysis is performed for the MIMO channel realization being given. The transmitter is notified of the selected codes for the each layer via the feedback channel, and uses the selected codes for the following transmission, assuming that the channel state information (CSI) stays the same at least over two consecutive frames. Finally, this paper evaluates the throughput performances of the ML-BICM ARQ and AC techniques using multidimensional field measurement data. The performance tendencies are correlated with propagation properties, obtained as results of the high-resolution channel analysis, such as spatial spreads at the transmitter and receiver sides.
Architecture, design, and test of continuous-time tunable intermediate-frequency bandpass delta-sigma modulators

This paper examines the architecture, design, and test of continuous-time tunable intermediate-frequency (IF) fourth-order bandpass delta-sigma (BP /spl Delta//spl Sigma/) modulators. Bandpass modulators sampling at high IFs (/spl sim/100 MHz) allow direct sampling of the RF signal-reducing analog hardware and make it easier to realize completely software programmable receivers. This paper presents circuit design of and test results from continuous-time fourth-order BP /spl Delta//spl Sigma/ modulators fabricated in AlInAs/GaInAs heterojunction bipolar technology with a peak unity current gain cutoff frequency (f/sub T/) of 80 GHz and a maximum frequency of oscillation (f/sub MAX/) of about 130 GHz. Operating from /spl plusmn/5-V power supplies, a fabricated 180-MHz IF fourth-order /spl Delta//spl Sigma/ modulator sampling at 4 GS/s demonstrates stable behavior and achieves 75.8 dB of signal-to-(noise+distortion)-ratio (SNDR) over a 1-MHz bandwidth. Narrowband performance (/spl sim/1 MHz) performance of these modulators is limited by thermal/device noise while broadband performance (/spl sim/60 MHz), is limited by quantization noise. The high sampling frequency (4 GS/s) in this converter is dictated by broadband (60 MHz) performance requirements.
Effects of approximating back emf of a brushless DC motor from trapezoidal to sinusoidal

Modelling of brushless dc motors has received wide attention in literature over the last three decades. Many of the reported works in this area have approximated the trapezoidal back emf of the machine as sinusoidal. This work investigates the effects of this approximation on average torque, torque ripple, rms torque, % torque ripple, speed ripple, rms speed, % speed ripple and rms current. The result show that the parameters vary with load except for average torque and speed. The rippling components vary drastically with load and the approximation doesn't have any prominent effect on dynamic torque vs. speed characteristics.
2-D large-signal modeling of VKP-8291A using TESLA

A P-band klystron built for the Spallation Neutron Source (SNS) application was modeled using TESLA to predict small- and large-signal performances of the device. This particular klystron has six cavities, including one second-harmonic cavity. The cavity parameters as measured in the cold-test experiment were entered as input to TESLA. Measured axial magnetic field as simulated by Maxwell 2-D was input to the code. This paper presents the TESLA output, including particle trajectory plots, electric field plots at different gaps, and the variation of output power with time steps for the device under consideration. Results will also be presented on the effects of different input variables, such as the total number of axial grids, radial grids, and modes on the TESLA-predicted output power of the device.
An ought-to-do deontic logic for reasoning about fault-tolerance: the diarrheic philosophers

In the present paper we use a variation of a well-known example (dining philosophers) to illustrate how deontic logics can be used to specify, and verify, systems with fault- tolerant characteristics. Towards this goal, we first introduce our own version of a prepositional deontic logic, and then some of its most important meta properties are described. Our main goal is to show that our deontic formalism is suitable for use in practical examples, and also to prepare the ground for more inclusive formalisms.
History-Based Adaptive Modulation for a Downlink Multicast Channel in OFDMA Systems

In this paper we investigated the adaptive modulation strategies for Multicast service in orthogonal frequency division multiple access systems. We defined a Reward function as the performance optimization target and developed adaptive modulation strategies to maximize this Reward function. The proposed optimization algorithm varied the instantaneous BER constraint of each mobile Multicast receiver according to its individual cumulated BER, which resulted in a significant Reward gain.
Comparative analyses and characterisation of average-value models of a brushless DC motor

Dynamic performance of a brushless DC motor is greatly influenced by a controller and the controller design relies on average-value models, which are vital in small signal and large signal analysis of electromechanical systems. Average-value model (AVM) of a brushless DC motor can be constructed by averaging motor variables or by exploring its resemblance to a DC motor. AVM can also be constructed by averaging motor currents in rotor reference frame because of its simplicity in analysis. In the present study, the appropriateness of average-value models in designing controllers for brushless DC motor were investigated by comparing the time domain parameters of the models and experimental results. Results indicated that the average-value model of the brushless DC motor derived from rotor reference frame model was highly appropriate for controller design.
Camera and microphone array for 3D audiovisual face data collection

This paper proposes a novel camera/microphone array system capable of capturing dynamic facial expression video with synchronized speech and reconstructing realistic 3D face models from the data. Both hardware and software issues including camera calibration, video/audio synchronization, facial marker tracking and 3D shape reconstruction are considered. To our best knowledge, this system is the first camera/microphone array system that is able to capture high-resolution facial expression video with synchronized speech. The system can be used to collect dynamic 3D audiovisual face data for many multimedia applications.
Efficient initialization of Mixtures of Experts for human pose estimation

This paper addresses the problem of recovering 3D human pose from a single monocular image. In the literature, Bayesian Mixtures of Experts (BME) was successfully used to represent the multimodal image-to-pose distributions. However, the expectation-maximization (EM) algorithm that learns the BME model may converge to a suboptimal local maximum. And the quality of the final solution depends largely on the initial values. In this paper, we propose an efficient initialization method for BME learning. We first partition the training set so that each subset can be well modeled by a single expert and the total regression error is minimized. Then each expert and gate of BME model is initialized on a partition subset. Our initialization method is tested on both a quasi-synthetic dataset and a real dataset (HumanEva). Results show that it greatly reduces the computational cost in training while improves testing accuracy.
Capacity of MIMO systems based on measured wireless channels

We measure the capacity of multiple-input multiple-output radio systems in microcellular environments. We use a new data evaluation method that allows to evaluate the cumulative distribution function of the capacity from a single measurement. This method is based on an extraction of the parameters of the multipath components and, thereafter, a synthetic variation of their phases. In the analyzed environments, we find capacities to be about 30% smaller than would be anticipated from an idealized model. In addition, the frequency selectivity of the channel makes the CDF of the capacity steeper and, thus, increases the outage capacity, compared with the frequency-flat case, but the influence on the mean capacity is small.