nils-herrmann/clean_scirepeval huggingface dataset:
Combinatorial Merging and Huffman's Algorithm

Huffman's algorithm produces an optimal weighted r-ary tree on a given set of leaf weights, where the weight of any parent node is the maximum of the son weights plus some positive constant. If the weights are viewed as (parallel) completion times, the algorithm has useful applications to combinatorial circuit design— especially for merging, or "fanning-in," a set of inputs with varying ready times: the weight of the tree's root node is then the completion time of the whole merging process. In this note we give new, tight upper and lower bounds on the weight of this root node (extending some work of Golumbic), and briefly describe an application in multiplexor design which exercises both of these bounds.
A general framework for sampling on the medial axis of the free space

We propose a general framework for sampling the configuration space in which randomly generated configurations, free or not, are retracted onto the medial axis of the free space. Generalizing our previous work, this framework provides a template encompassing all possible retraction approaches. It also removes the requirement of exactly computing distance metrics thereby enabling application to more realistic high dimensional problems. In particular, our framework supports methods that retract a given configuration exactly or approximately onto the medial axis. As in our previous work, exact methods provide fast and accurate retraction in low (2 or 3) dimensional space. We also propose new approximate methods that can be applied to high dimensional problems, such as many DOF articulated robots. Theoretical and experimental results show improved performance on problems requiring traversal of narrow passages. We also study tradeoffs between accuracy and efficiency for different levels of approximation, and how the level of approximation effects the quality of the resulting roadmap.
A Curvature-Based Approach to Terrain Recognition

The authors describe an algorithm which uses a Gaussian and mean curvature profile for extracting special points on terrain and then use these points for recognition of particular regions of the terrain. The Gaussian and mean curvatures are chosen because they are invariant under rotation and translation. In the Gaussian and mean curvature image, the points of maximum and minimum curvature are extracted and used for matching. The stability of the position of those points in the presence of noise and with resampling is investigated. The input for this algorithm consists of 3-D digital terrain data. Curvature values are calculated from the data by fitting a quadratic surface over a square window and calculating directional derivatives of this surface. A method of surface fitting which is invariant to coordinate system transformation is suggested and implemented. The algorithm is tested with and without the presence of noise, and its performance is described.
Color image edge detection using cluster analysis

A color image edge detection algorithm is proposed based on the idea that use global color information to guide local gradient computation. The major chromatic components of an image are first extracted through cluster analysis. According to these color clusters, a set of linear chromatic transforms are generated. An appropriate chromatic transform is chosen for each pixel to maximize the gradient magnitude. In this way, edges are treated as transitions from one cluster to another. The algorithm is implemented and experimental results for real color images are included.
Effects of random circuit fabrication errors on small signal gain in a traveling wave tube

Random fabrication errors due to fabrication tolerances in the manufacture of slow wave circuits may have detrimental effects on the performance of traveling-wave tubes (TWTs) of all types. Such errors will pose an increasingly serious problem as TWTs are designed and built to operate in the sub-millimeter wavelength regime and beyond. In this paper we present analytical and numerical results on the expected degradation of the small signal gain of a TWT when small random, axially varying perturbations are present in the circuit phase velocity. A new scaling law for the reduction in the ensemble-average gain is derived from the third order ordinary differential equation with randomly varying coefficients that governs the beam-wave interaction in a TWT in the presence of small errors. Analytical results for the average gain reduction compare favorably over a broad range of Pierce parameter values with results from numerical integrations of the differential equation. The effects of random errors on the output phase will also be discussed in our presentation.
Combining Chunk Boundary and Chunk Signature Calculations for Deduplication

Many modern, large-scale storage solutions offer deduplication, which can achieve impressive compression rates for many loads, especially for backups. When accepting new data for storage, deduplication checks whether parts of the data is already stored. If this is the case, then the system does not store that part of the new data but replaces it with a reference to the location where the data already resides. A typical deduplication system breaks data into chunks, hashes each chunk, and uses an index to see whether the chunk has already been stored. Variable chunk systems offer better compression, but process data byte-for-byte twice, first to calculate the chunk boundaries and then to calculate the hash. This limits the ingress bandwidth of a system. We propose a method to reuse the chunk boundary calculations in order to strengthen the collision resistance of the hash, allowing us to use a faster hashing method with fewer bytes or a much larger (256 times by adding two bytes) storage system with the same high assurance against chunk collision and resulting data loss.
Effects of impulse noise on the performance of multidimensional parity check codes

This paper investigates the effects of impulse noise on the performance of multidimensional parity check codes (MDPC) in additive white Gaussian noise and Rayleigh fading channels. The Bhattacharya upper bound is derived for impulsive noise and is found to closely match with the simulation results. It is observed that impulse noise can degrade the bit error rate performance by several dBs. Further, the use of incorrect impulse noise model in the receiver decoding algorithm is found to cause severe penalty in fading channels. Our results also show that MDPC codes outperform traditional convolutional codes in impulsive noise channels. The performance of MDPC codes is found to be slightly inferior to turbo codes, when the latter uses slightly lower code rate and higher complexity. The capacity limits for impulsive noise channels are also calculated
A modular telerobotic task execution system

A telerobot task execution system, TTES, is proposed to provide a general parametrizable task execution capability. The system includes communication with the calling system, e.g., a task planning system, and single- and dual-arm sensor-based task execution with monitoring and reflexing. A specific task is described by specifying the parameters to various available task execution modules including trajectory generation, compliance control, teleoperation, monitoring, and sensor fusion. Reflex action is achieved by finding the corresponding reflex action in a reflex table when an execution event has been detected with a monitor
An Ontology-Based Semantic Foundation for Organizational Structure Modeling in the ARIS Method

This paper focuses on the issue of ontological interpretation for the ARIS organization modeling language with the following contributions: (i) providing real-world semantics to the primitives of the language by using the UFO foundational ontology as a semantic domain, (ii) the identification of inappropriate elements of the language, using a systematic ontology-based analysis approach, and (iii) recommendations for improvements of the language to resolve the issues identified.
Dimensionality reduction of mass spectrometry imaging data using autoencoders

The use of mass spectrometry imaging (MSI) techniques has become a powerful tool in the fields of biology, pharmacology and healthcare. Next generation experimental techniques are able to generate 100s of gigabytes of data from a single image acquisition and thus require advanced algorithms in order to analyse these data. At present, analytical work-flows begin with pre-processing of the data to reduce its size. However, the pre-processed data is also high in dimensionality and requires reduction techniques in order to analyse the data. At present, mostly linear dimensionality reduction techniques are used for hyper-spectral data. Here we successfully apply an autoencoder to MSI data with over 165,000 pixels and more than 7,000 spectral channels reducing it into a few core features. Our unsupervised method provides the MSI community with an effective non-linear dimensionality reduction technique which includes the mapping to and from the reduced dimensional space. This method has added benefits over methods such as PCA by removing the need to select meaningful features from the entire list of components, reducing subjectivity and significant human interaction from the analysis.
Controller technique for robust conflict free sequential flight mode control

Fre Flight concept is a new air traffic management paradigm that has been proposed by the avionics community to solve the problem of air traffic congestion. But the greatest challenge in realizing such a concept is conflict detection and resolution during the free flight of aircraft. In present scenario, during any conflicts or disturbances, the autopilot fails and the pilot has to take over the flight control. Therefore the requirement of a robust control system which performs well even in the presence of disturbances and uncertainties, for conflict avoidance maneuvers during free flight is of great interest. In this paper, for conflict resolution, the entire flight path is split into different discrete modes and an integrated sliding mode plus state feedback controller approach is applied to develop a robust control law for velocity, heading angle and position tracking in each mode. The ability of the controller to provide a conflict-free trajectory through different flight modes is also being validated.
Characterization of a Ka-band Sheet-Beam Coupled-Cavity Slow-Wave Structure

This paper investigates the properties of a three-slot doubly periodic staggered-ladder sheet-beam coupled-cavity slow-wave structure (SWS) developed at the U.S. Naval Research Laboratory. The structure is overmoded with complicated mode crossings and field structures. The staggered-ladder structure is compared to round-beam structures via full-wave electromagnetic simulations and experimental measurements. We explore the application of this SWS in a traveling-wave tube amplifier.
Annealed dropout training of deep networks

Recently it has been shown that when training neural networks on a limited amount of data, randomly zeroing, or “dropping out” a fixed percentage of the outputs of a given layer for each training case can improve test set performance significantly. Dropout training discourages the detectors in the network from co-adapting, which limits the capacity of the network and prevents overfitting. In this paper we show that annealing the dropout rate from a high initial value to zero over the course of training can substantially improve the quality of the resulting model. As dropout (approximately) implements model aggregation over an exponential number of networks, this procedure effectively initializes the ensemble of models that will be learned during a given iteration of training with an enemble of models that has a lower average number of neurons per network, and higher variance in the number of neurons per network-which regularizes the structure of the final model toward models that avoid unnecessary co-adaptation between neurons. Importantly, this regularization procedure is stochastic, and so promotes the learning of “balanced” networks with neurons that have high average entropy, and low variance in their entropy, by smoothly transitioning from “exploration” with high learning rates to “fine tuning” with full support for co-adaptation between neurons where necessary. Experimental results demonstrate that annealed dropout leads to significant reductions in word error rate over standard dropout training.
A high speed dynamic power supply current sensor

An on-chip wideband CMOS transient supply current sensor is presented. The sensor permits on-chip transient supply current testing (iDDT) for circuits where supply current transients associated with circuit operation are measured and evaluated. The sensor was designed and fabricated in 0.5-mum CMOS technology and was physically demonstrated at 200 MHz sensing speeds. Measured results from the sensor show it is possible to distinguish between normal and faulty logic inverter operation. Extensions to the sensor design are presented including autozero circuitry to cancel MOS transistor mismatch.
Application of the channel synthesis approach to evaluate the performance of an experimental 4-port application antenna

This paper presents an approach to compare the MIMO performance of arbitrary antenna arrays in realistic propagation scenarios without the need of multiple measurement campaigns. High resolution parameter estimation is used to obtain antenna independent characterizations of the mobile radio channel. Subsequently the estimation results are used to embed the influence of application antennas into the radio channel. A novel approach of modeling and estimating the spatial properties of diffuse scattering is presented and validated. The feasibility of the approach is demonstrated using MIMO channel sounder measurements and an experimental 4-port application antenna.
An additive approach to transform-domain information hiding and optimum detection structure

This paper presents an additive approach to transform-domain information hiding and the performance analysis for images and video. The watermark embedding method is designed to satisfy the perceptual constraints and improve the detectability as well as the information embedding rate. The statistical behaviors of subband coefficients are modeled by the generalized Gaussian distribution. The structure of the optimum detection is built and the performance of the exact asymptotic detection is evaluated using large deviation theory. Our approach can not only achieve good transparency but also precisely control the detection errors. It can be applied to watermarking, authentication, fingerprinting, and steganography.
Detection of Unusual Motion Using Computer Vision

In this paper, we propose different criteria for detecting unusual motion in surveillance cameras. Initially, a certain environment is observed within a time interval, and captured trajectories are used as examples of usual trajectories. These trajectories are used to build a spatial occupancy map (SpOM, which is introduced in this paper) of the observed people, as well as main flow directions. In the test period, each new trajectory is classified as normal or unusual with respect to spatial occupancy and trajectory consistency. The spatial occupancy criterion considers the relation of space occupancy between the new tracked trajectory and the observed period. The trajectory consistency criterion considers the agreement of the new trajectory with the main flows extracted in the training period. Experimental results showed that these criteria can be used as an automatic pre-screening of suspect motion in surveillance applications
Burn-in failures and local region yield: an integrated field-reliability model

Defects have long been known to cluster on semiconductor wafers. Recent research has shown that this fact may be exploited to produce die of high reliability, (i.e. decreased infant mortality), by sorting die into bins based on how many of their neighbors test faulty. Die that test good at wafer probe, yet come from neighborhoods with many faulty die, have a higher incidence of infant mortality failure than die from neighborhoods with few faulty die. Analysis of burn-in results from the SEMATECH test methods experiment suggests that such a binning approach has the potential to isolate a high quality bin that displays very few burn-in failures. This paper presents the first analytical model that quantifies the reliability improvement one might expect when binning die based on local region yield.
DoA resolution limits in MIMO channel sounding

Several high-resolution parameter estimation algorithms have been applied to directional channel parameter estimation in channel sounding. However, the variance and reliability of the estimation results are not often clearly defined in practical environments. Especially, imperfections, mutual coupling, and residual calibration errors of real antenna arrays impose limits to the DoA parameter estimation performance. One critical example is the resolution of coherent wave fronts which is often the case in real-time channel sounding. We derive the Cramer-Rao lower bound (CRLB) of an unbiased direction-of-arrival (DoA) estimator. The CRLB indicates the minimum achievable parameter variance which cannot be outperformed by any parameter estimator. It is shown that the required derivatives of the observations with respect to the DoA and path weight parameters can be deduced from a properly defined calibration data model.
Dynamic occlusion handling in the PHD filter for range-only tracking: Proof of concept

When localizing multiple tag-free targets using ultra-wideband sensors, targets closer to the sensor occlude targets further from the sensor and in turn these targets are not detected. In this paper we first model the occlusion region of each target. Based on this model, targets unresolved in sensor data and occluded by other targets are updated. Experimental results demonstrate that the incorporation of occlusion handling leads to a more complete belief of the scenario and use of fewer sensors for covering the scenario.
A low-power 72.8-GHz static frequency divider in AlInAs/InGaAs HBT technology

We report a 72.8-GHz fully static frequency divider in AlInAs/InGaAs HBT IC technology. The CML divider operates with a 350-mV logic swing at less than 0-dBm input power up to a maximum clock rate of 63 GHz and requires 8.6 dBm of input power at the maximum clock rate of 72.8 GHz. Power dissipation per flip-flop is 55 mW with a 3.1-V power supply. To our knowledge, this is the highest frequency of operation for a static divider in any technology. The power-delay product of 94 fJ/gate is the lowest power-delay product for a circuit operating above 50 GHz in any technology. A low-power divider on the same substrate operates at 36 GHz with 6.9 mW of dissipated power per flip-flop with a 3.1-V supply. The power delay of 24 fJ/gate is, to our knowledge, the lowest power-delay product for a static divider operating above 30 GHz in any technology. We briefly review the requirements for benchmarking a logic family and examine the historical trend of maximum clock rate in high-speed circuit technology.
20GHz low power CMOS single chip receiver using resonant transformer techniques for enhanced demodulation efficiency

Smart Dust Systems (SDSs) are typically very low cost wireless ad-hoc networks distributed over a relatively small area which sense, transmit and receive signals. They can be used in applications such as health monitoring, environment sensing and temporal wireless services . The distributed network is composed of wireless nodes, each including antennas, transceivers and digital controllers. Minimizing both cost and power are important requirements for SDS applications. SDSs also require that each node has very small dimensions that are on the cubic millimeter scale. It is difficult to fit an antenna into such a small volume. For example, at 2 GHz, a half-wave dipole antenna is 7.5cm . Using low loss dielectric material, the state of the art FICA (F-Inverted Compact Antenna) at 2.2 GHz has a size of 12mm × 12mm × 3mm . This is still larger than the SDS requirement. Since the chip size is commonly a few mm2, it is clear the antenna size dominates the system size. High frequency operation is a promising way to reduce antenna size since it is inversely proportional to the carrier frequency. For example, theoretically a FICA antenna can be scaled to millimeter range at 20 GHz. To achieve the millimeter size requirements, while also minimizing cost, in this work, we take advantage of CMOS scaling to design an inexpensive 0.13µm CMOS-based amplitude shift keying (ASK) radio frequency receiver that operates at 20 GHz. Resonant transformer circuits which increase voltage levels have also been used to improve system sensitivity [1,3].
3D intracranial artery segmentation using a convolutional autoencoder

Automated segmentation of intracranial arteries on magnetic resonance angiography (MRA) allows for quantification of cerebrovascular features, which provides tools for understanding aging and pathophysiological adaptations of the cerebrovascular system. Using a convolutional autoencoder (CAE) for segmentation is promising as it takes advantage of the autoencoder structure in effective noise reduction and feature extraction by representing high dimensional information with low dimensional latent variables. In this paper, we trained an 8-layer CAE to learn a 3D segmentation model of intracranial arteries from 49 cases of MRA data. After parameter optimization and prediction refinement, our trained model was shown to perform better than the three traditional segmentation methods in both binary classification and visual evaluation.
Best-fit mobile recharge pack recommendation

In today's competitive market, mobile service providers are very keen on improving the customer satisfaction by providing personalized services. Recommending recharge packs to the subscribers that suits their personal profile is an important such personalized service. But a solution to this problem is not that simple as it requires careful analysis of the subscribers' usage behavior and involves very large volume of data generated by the subscribers' frequent interaction with the telecom network. Also, this solution needs to ensure a fine balance between customer satisfaction and profitability of service providers. This paper discusses about an adaptive recommendation model which overcomes various deficiencies associated with existing solutions. The model recommends suitable recharge packs to subscribers based on their usage history and affordability. Further, it accommodates a configurable fairness parameter that ensures a balance between the profitability factor, conversion probability and relevance of the recommendations. Due to the sheer volume of the data involved, the model is implemented using a distributed framework. The validity of the model is evaluated on the basis of statistical properties and conversion factor.
Audio-Visual Speaker Localization Using Graphical Models

In this work we propose an approach to combine audio and video modalities for person tracking using graphical models. We demonstrate a principled and intuitive framework for combining these modalities to obtain robustness against occlusion and change in appearance. We further exploit the temporal correlations that exist for a moving object between adjacent frames to account for the cases where having both modalities might still not be enough, e.g., when the person being tracked is occluded and not speaking. Improvement in tracking results is shown at each step and compared with manually annotated ground truth