nils-herrmann/clean_scirepeval huggingface dataset:
A 10.24GSPS photonic sampled bandpass /spl Delta//spl Sigma/ modulator direct-sampling at 12GHz

A 10.24GSPS photonic sampled DeltaSigma modulator was implemented with HRL laboratories InP HBT process. The 12GHz analog input is sampled by a LiNbO3 optical modulator with a 10.24GHz ultra-low jitter mode-locked laser. The intensity-modulated differential laser pulses are converted into electronic charges by on-chip GaInAs photo diodes and directly integrated into the first stage integrator of a 4th-order continuous-time bandpass DeltaSigma modulator with a passband centered at 1.8GHz. The high notch frequencies of the DeltaSigma ADC are achieved by using a special type of integrator. This ADC achieves 59.9dB SNDR in 1MH bandwidth with 1263 InP HBT transistors
Design of a novel mini notched turbine with optimized power management circuit

This paper proposes a novel mini notched turbine that can be used as feasible energy source for medical systems, aerospace, car, and home applications, or could be converted into a maintenance free energy generator system, of continuous and sustainable operation. The micro system developed in this work is an innovation in turbine designs, and has special characteristics such as: novel mini notched blades, which contribute to increased rotor speed, and an innovative turbine casing, that combine benefits of the impulse and reaction turbine models. Also, paper presents a methodology for optimizing ultralow power management circuit to improve the efficiency of DC-DC converter, this is accomplished using particle swarm optimization technique, where the converter efficiency is used as the fitness function and inductor and on-time are chosen as optimized parameters. This design improves the DC-DC converter efficiency and the efficiency of power management circuit by 10% comparing with conventional power management circuits over a wide range of input power, allowing harvesting more power and delivering it to load. This approach is not limited to ultra low power applications; it can easily be extended in portable applications to extend the battery life.
Effect of velocity spread on operation of high power gyrotrons

The effect of velocity spread on the operation of 140 GHz gyrotrons has been studied. The performance of two cavities, with low and high Q, has been examined experimentally and theoretically. The simulation code MAGY was modified to include the measured velocity distribution function and the measured pitch angle in order to compare the measured efficiencies with the predicted efficiencies. Based on measurements, the inferred velocity spread at a beam current of 40 A is given by (/spl delta/v/sub /spl perp///v/sub /spl perp//) RMS=15%. Simulations with this spread produced efficiencies lower than those measured. However, it was found that assuming /spl delta/v/sub /spl perp///v/sub /spl perp//=10% for 40 A current and using the experimentally determined dependence of the spread on the current the calculated efficiencies agree well with the measured efficiencies for the low 8 cavity. The efficiency of the low Q gyrotron at 40 A beam current is only 27%. For the same beam current and velocity spread the efficiency of the high Q gyrotron was predicted to be 40% which agrees well with the measured efficiency of 39%.
Energy efficient reconfiguration for practical load combinations in distribution systems

Practically, loads are found to be voltage dependent and reconfiguration in distribution systems changes the node voltage profile. Hence reconfiguration affects the load behaviour. In this scenario, the traditional approach of defining energy efficient operation as mere loss minimisation does not give truly energy efficient results. Therefore this study defines energy efficiency as a multi-variable function in terms of energy efficiency defining parameters and develops an energy efficiency performance index, in order to identify an energy efficient configuration. The proposed approach of reconfiguration gives more flexibility to utilities in operating the distribution system with improved power quality and reliability, and enables them to actively participate in a competitive deregulated environment. In this study, voltage dependent loads are modelled in the form of load combinations to represent the real world loads at a bulk delivery point of the distribution system.
A rapid prototyping co-simulation environment for neural control

A flexible approach to the development of control systems containing artificial neural networks (ANNs) is to construct a co-simulation involving the two development packages of choice, where each provides the appropriate system components and they execute concurrently. This has advantages over the small number of control system development packages which support ANNs, in that the ANN components are provided by a specialist package which can offer more comprehensive facilities than would otherwise be available. This co-simulation approach has been implemented on a Unix system using two fully featured packages which have a large user base in their respective areas of applicability. One control scheme which has been investigated is indirect adaptive control and the implementation of this scheme within the co-simulation is described. Two examples of its operation are given; one based on a simple linear control problem, and another based on a nonlinear helicopter mathematical model. Construction of a neural control simulation environment by integration of two proprietary packages appears to be a highly flexible approach. It enables a potentially wide range of different control strategies to be examined, without recourse to explicit programming, and by using ANN code generation, allows rapid prototyping of ANN controllers to take place. In principle, this approach can be utilised for cost-effective investigation of other promising technologies where inadequate support for control system simulation exists.
A complete space-charge model for 3D structures in the CHRISTINE 3D large-signal code

The CHRISTINE 3D code was developed to simulate the large-signal characteristics of slow-wave devices using a fast, parametric model. The model includes a fully three-dimensional representation of both particle motion and electromagnetic fields, treating external fields, the traveling-wave circuit field, and the RF space-charge field separately, but self-consistently. In common with existing parametric large-signal models, the space-charge fields (due to the beam charges) are computed assuming that they exist only within a cylindrical pipe at the inner radius of the circuit structures. In this paper, we develop a method for correcting the space-charge field to take account of the true 3D geometry. The correction terms are pre-computed using the 3D electromagnetic simulation code CTLSS, and the additional fields are included in the non-linear simulation. The corrected model shows a reduction in predicted gain.
A small-angle approximation for bistatic polarimetry

Scattering matrices recorded with bistatic polarimetric radar systems can be described using bistatic Huynen parameters. These parameters not only depend on the properties of the target, but also on the bistatic angle. Quasi-monostatic radars as well as many bistatic radars exhibit configurations limited to small bistatic angles between receiver and transmitter. Therefore it is possible to apply a small-angle approximation to some of the Huynen parameters. In this paper the impact of this approximation on scattering matrices is investigated and the consequences of the approximation are discussed. From the discussion follows that for small bistatic angles the scattering matrix can be interpreted and handled like a monostatic scattering matrix apart from a perturbation along its antidiagonal. The findings are supported by simulation results.
Antenna-coupled dual band RF energy harvester design

Radio Frequency (RF) energy harvesting aims at collecting and converting ambient RF wave energy into storable electrical energy to power electronics. The reported RF energy density in urban areas can be as high as 0.5μW/cm2 which corresponds to an input power level of 16.6 μW (−17.6 dBm) at 1800MHz . It is thus appealing to convert the RF energy in the environment into electrical energy and use it to directly power mobile devices, remote sensors data loggers, low power digital applications, or charge batteries . In comparison with conventional battery-based power sources, the RF energy as a source is ubiquitous and can be harvested wirelessly. In addition, the harvesting of RF energy makes use of the amount of energy that will be otherwise wasted. Therefore, it makes use of the RF energy more efficient and is an environmentally friendly practice.
Accurate representation of attenuation in large - signal helix TWT simulation codes

We report on the results of a comparison study of the CHRISTINE 1D helix TWT simulation code with the small signal theory of a beam interacting with a slow wave supported by a sheath helix in the presence of loss, in order to ascertain whether attenuation is treated with sufficient accuracy by the ad hoc model used in the CHRISTINE code. This study was motivated in part by the fact that losses in both dielectrics and in metals generally increase with increasing operating frequency, making the accuracy of the CHRISTINE code a potential concern for mm-wave helix TWT design. The basic conclusion of this study is that the existing model of attenuation in CHRISTINE is sufficiently accurate for attenuation rates up to a few dB/pitch. For larger attenuation rates, accuracy can be further improved by taking into account the (quadratic) dependence of phase velocity on the attenuation rate.
Enhancing classification of mass spectrometry imaging data with deep neural networks

Mass spectrometry imaging (MSI) determines the spatial distribution of thousands of molecules and chemical species simultaneously and has emerged as a powerful suite of tools in pathology, pharmaceuticals, healthcare and material science applications. MSI experiments typically produce between 104 and 106 pixels, each of which contain a full mass spectrum. The use of MSI in pathological applications may involve classification of tissues based on the spectral information, though to date there have been no systematic studies of classification algorithms, comparison of performance when using the full versus reduced dimensionality of the data, or investigation into multi-class classification problems. Here we evaluate a number of algorithms for classifying regions in a MSI dataset before and after unsupervised non-linear dimensionality reduction with a deep neural network. We evaluate the performance of each algorithm with eight metrics in both the high (1,601) and low (3) dimensional feature space. Our results show that in the high dimensional space, only a Softmax classifier and support vector machine (SVM) with a linear kernel, perform to a satisfactory level, with all other algorithms overfitting the training data and performing poorly on the testing data. We also observe that that multi-class classification performance is drastically improved by the non-linear reduction with the deep neural network, improving scores and reducing variation between classes compared with the original high dimensional data. In the low dimensional space, the best performance observed is from a Decision Tree, though KNN, SVM (Gaussian kernel) and Softmax classifiers also perform well, scoring over 0.93 across all metrics and classes.
Directional analysis of multipath propagation in vehicle-2-vehicle channels

Vehicle-to-vehicle (V2V) communication links differ significantly from conventional cellular deployments, since both link ends are moving and the antennas are close to the ground level. For reliable performance predictions accurate channel models are required. Based on two different V2V channel sounding campaigns the directional multipath propagation characteristics are studied allowing geometry based stochastic channel model parametrization. The results are focused on analysis of the multipath cluster statistics and the double directional multipath behavior.
Audio-Visual Speech Fusion Using Coupled Hidden Markov Models

The fusion of audio and visual speech is an instance of the general sensory fusion problem. The sensory fusion problem arises in the situation when multiple channels carry complementary information about different components of a system. In the case of audio-visual speech, the two modalities manifest two aspects of the same underlying speech production process. From an observer's view, the audio channel and the visual channel represent two interacting stochastic processes. We seek a framework that can model the two individual processes as well as their dynamic interactions. One interesting aspect of audio-visual speech is the inherent asynchrony between the audio and visual channels. Most early integration approaches to the fusion problem assume tight synchrony between the two. However, studies have shown that human perception of bimodal speech does not require rigid synchronization of the two modalities. Furthermore, humans appear to use the audio-visual asynchronies as multimodal features. For example, it is well known that the voice onset time is an important cue to the voicing feature in stop consonants. This information can be conveyed bimodally by the interval between seeing the stop release and hearing the vocal cord vibration. Therefore, a successful fusion scheme should not only be tolerant to asynchrony between the audio and visual cues, but also be apt to capture and exploit this bimodal feature.
A Low Power Asynchronous GPS Baseband Processor

We present the design and implementation of an asynchronous Global Positioning System (GPS) base band processor architecture designed with a combination of Quasi-Delay-Insensitive (QDI) and bundled-data techniques, with a focus on minimizing power consumption. All subsystems run at their natural frequency without clocking and all signal processing is done on-the-fly. Transistor-level simulations show that our system consumes only 1.4mW with position 3-D rms error below 4 meters, comparing favorably to other contemporary GPS base band processors.
Effects of Mutual Coupling upon the LMS Adaptive Antenna Array Algorithm

The Least Mean Squares (LMS) algorithm has been frequently used for adapting the weighting multipliers of array elements which form the resulting antenna pattern. While significant performance gain has been reported for such applications as forming a pattern maximum in the direction of a desired signal and adaptively nulling strong jammers, many of the current results have assumed no mutual coupling between array elements. This paper examines the effects of mutual element coupling upon two properties of the LMS algorithm: (1) convergence rate, and (2) steady state pattern. Analysis is developed which displays the effects of mutual coupling explicitly via the elemental coupling coefficients. It is shown that both amplitude and phase of the elemental coupling in general degrades the convergence rate of the LMS algorithm and causes the pattern maximum to appear at a look-direction other than the angle of desired signal arrival. Several examples then illustrate LMS performance in representative coupled environments.
A Scalable Cloud Computing Infrastructure for Geospatial Data Analytics for Change Detection

Geospatial data analytics is emerging as a promising technology in decision support systems in many application domains. This paper presents results from a study on the use of elastic cloud computing to provide geospatial data analytics in an efficient way. The analytics is applied to bulks of satellite data from multi-spectral optical sensors. The system design includes data integration, data caching, and a job manager to orchestrate the data analytics workflow. The system is validated in a case study focusing on precision agriculture, where a simple harvest detection algorithm is implemented to identify the time of harvest for crop fields. The performance of the underlying cloud computing infrastructure is evaluated and the benefit of scalingthe cloud horizontally when bulk satellite data becomes available is discussed.
Effect of time delay on telesurgical performance

In the area of surgical robotics no standard means of performance evaluation has been established. Thousands of surgeons have gone through the SAGES FLS Program, and the psychomotor skill portion of the program is considered the gold standard in laparoscopic skills evaluation. This research describes the use of the FLS Block Transfer task to evaluate the performance of both surgeons and non-surgeons teleoperating under different time delay conditions on the University of Washington RAVEN Surgical Robot. Time delays of 0ms, 250ms, and 500ms were used and a statistically significant difference in mean block transfer time as well as mean tool tip path length were shown. For this task no significant difference was shown between the surgeon and non-surgeon groups. Clearly surgeon input and feedback is key to surgical robotic system development, but this result implies that non-surgeon subjects can be tested for simple usability evaluations.
Efficient image contour detection using edge prior

An effective and efficient image contour detector is highly desired due to its wide applications in computer vision and multimedia retrieval. However, the state-of-the-art image contour detection algorithms are very computationally intensive, and thus impractical for web-scale applications. In this work, we study the relationship between edge detection and contour detection, based on which an edge-based image contour detection algorithm is proposed. This algorithm fully makes use of cheap edge information for efficiency purpose. The experiments on benchmark data sets show that, the proposed contour detector works much faster than existing state-of-the-art algorithms while maintaining high accuracy, and thus suitable for large-scale applications.
AutoFLox: An Automatic Fault Localizer for Client-Side JavaScript

Java Script is a scripting language that plays a prominent role in modern web applications today. It is dynamic, loosely typed, and asynchronous. In addition, it is extensively used to interact with the DOM at runtime. All these characteristics make Java Script code error-prone and challenging to debug. Java Script fault localization is currently a tedious and mainly manual task. Despite these challenges, the problem has received very limited attention from the research community. We propose an automated technique to localize Java Script faults based on dynamic analysis of the web application, tracing, and backward slicing of Java Script code. Our fault localization approach is implemented in an open source tool called Auto Lox. The results of our empirical evaluation indicate that (1) DOM-related errors are prominent in web applications, i.e., they form at least 79% of reported Java Script bugs, (2) our approach is capable of automatically localizing DOM-related Java Script errors with a high degree of accuracy (over 90%) and no false-positives, and (3) our approach is capable of isolating Java Script errors in a production web application, viz.,
Atmospheric pressure plasma activation of plastics and composites for improved adhesion

Atmospheric pressure plasma treatment is a key process for surface preparation prior to adhesive bonding of plastics and composites for use in the aerospace, automotive, medical device, and electronics industries. A review is presented on methods for surface preparation of polymers and composites using low-temperature, atmospheric pressure radio frequency helium and oxygen plasmas. This is a promising technique for replacing traditional methods of surface preparation such as peel ply and abrasion. The plasma source generates an afterglow containing ∼1×1017 cm−3 oxygen atoms, ∼1×1017 cm−3 metastable oxygen molecules (1Δ g ), and ∼4×1014 cm−3 ozone molecules. With sufficient exposure to the afterglow, polymer and composite surfaces are fully activated such that when bonded and cured with epoxy adhesives, they undergo 100% cohesive failure. Depending on the material, lap shear strength and delamination resistance can be increased over 50% from that activated by solvent wiping or abrasion. Surface analysis by water contact angle and AFM indicates that there is no correlation between bond strength and surface energy or surface roughness. Instead, bond strength correlates with the fraction of the polymer surface sites that are oxidized and converted into carboxylic acid groups, as determined by x-ray photoemission and infrared spectroscopy. Polymers with aromatic groups in the backbone are especially suitable for activation with the atmospheric pressure plasma. The activation mechanism, surface characterization results, and bonding strength improvement are discussed in detail.
Content-Based Image Retrieval by Feature Adaptation and Relevance Feedback

The paper proposes an adaptive retrieval approach based on the concept of relevance-feedback, which establishes a link between high-level concepts and low-level features, using the user's feedback not only to assign proper weights to the features, but also to dynamically select them within a large collection of parameters. The target is to identify a set of relevant features according to a user query while at the same time maintaining a small sized feature vector to attain better matching and lower complexity. To this end, the image description is modified during each retrieval by removing the least significant features and better specifying the most significant ones. The feature adaptation is based on a hierarchical approach. The weights are then adjusted based on previously retrieved relevant and irrelevant images without further user-feedback. The algorithm is not fixed to a given feature set. It can be used with different hierarchical feature sets, provided that the hierarchical structure is defined a priori. Results achieved on different image databases and two completely different feature sets show that the proposed algorithm outperforms previously proposed methods. Further, it is experimentally demonstrated that it approaches the results obtained by state-of-the-art feature-selection techniques having complete knowledge of the data set.
An Information Extraction Model for Unconstrained Handwritten Documents

In this paper, a new information extraction system by statistical shallow parsing in unconstrained handwritten documents is introduced. Unlike classical approaches found in the literature as keyword spotting or full document recognition, our approch relies on a strong and powerful global handwriting model. A entire text line is considered as an indivisible entity and is modeled with Hidden Markov Models. In this way, text line shallow parsing allows fast extraction of the relevant information in any document while rejecting at the same time irrelevant information. First results are promising and show the interest of the approach.
A radio-frequency energy harvesting scheme for use in low-power ad hoc distributed networks

While RF energy harvesting has proven to be a viable power source for low-power electronics, it is still a challenge to obtain significant amounts of energy fast and efficiently from the ambiance. Available RF power is usually very weak, resulting in a weak voltage applied to a demodulator to drive it into a region of significant nonlinearity. An RF energy harvesting system consisting of a rectenna, a dc-dc voltage converter, and a novel battery cell is proposed. The rectenna is an integration of an antenna and rectifying diodes. In addition, a switched-capacitor dc-dc voltage converter is integrated on a silicon integrated circuit with energy transfer efficiency as high as 40.5%. A new battery system that can be recharged at low voltage (<; 1.2 V) is demonstrated. In this brief, all of these elements are tested as a system to achieve RF battery recharging from a commercially available hand-held communication device. The system exhibited an overall harvesting efficiency of 11.6%.