nils-herrmann/clean_scirepeval huggingface dataset:
Modelling of Parasitic Capacitances for Single-Gate, Double-Gate and Independent Double-Gate MOSFET

This paper discusses the type of capacitances for Single Gate MOSFET and Double Gate MOSFET including their quantity. The effect of parasitic capacitance makes double gate MOSFET more suitable component for the designing of digital logic switches than single gate MOSFET. Here, we introducing Independent double gate MOSFET operation based on VeSFET concept. Then introducing with the total capacitance model of Independent double gate MOSFET and compared its performance parameters with double gate MOSFET and the single gate MOSFET. Double gate transistor circuit is the first choice for reduction of short channel effect in application of MOSFET. The basic advantage of double gate MOSFET is its area required. But design CMOS double gate transistor for AND functionality suffering from high leakage current; while using Independent double gate MOSFET based on VeSFET concept reduction of that leakage current is possible. So, we can easily implement logic circuits while using CMOS design based on Independent double gate MOSFET which gives high performance.
Quantitative Evaluation of Induction Efficiency in Domestic Induction Heating Applications

Efficiency of electromagnetic energy transference in coupled pot-inductor systems of induction cooktops is analyzed. The influence of different parameters such as pot properties, number of turns of the windings, frequency of the currents, cable types, and ferrite arrangements is studied by means of an analytical model. The proposed model is a combination of two independent models corresponding to the loaded planar coil impedance and the cable losses, respectively. The two models are related using the global magnetic field of the system. The analysis reveals that it is not possible to experimentally determine induction efficiency by measuring unloaded and loaded winding impedances. In this paper, we propose a practical method to obtain the induction efficiency and compare the results with theoretical predictions. Quantitative results corresponding to different pot materials, frequencies, type of cables, and ferrite arrangements are also presented.
The evidence supporting the use of honey as a wound dressing.

Some clinicians are under the impression that there is little or no evidence to support the use of honey as a wound dressing. To allow sound decisions to be made, this seminar article has covered the various reports that have been published on the clinical usage of honey. Positive findings on honey in wound care have been reported from 17 randomized controlled trials involving a total of 1965 participants, and 5 clinical trials of other forms involving 97 participants treated with honey. The effectiveness of honey in assisting wound healing has also been demonstrated in 16 trials on a total of 533 wounds on experimental animals. There is also a large amount of evidence in the form of case studies that have been reported. It has been shown to give good results on a very wide range of types of wound. It is therefore mystifying that there appears to be a lack of universal acceptance of honey as a wound dressing. It is recommended that clinicians should look for the clinical evidence that exists to support the use of other wound care products to compare with the evidence that exists for honey.
Multi-agent quadrotor testbed control design: integral sliding mode vs. reinforcement learning

The Stanford Testbed of Autonomous Rotorcraft for Multi-Agent Control (STARMAC) is a multi-vehicle testbed currently comprised of two quadrotors, also called X4-flyers, with capacity for eight. This paper presents a comparison of control design techniques, specifically for outdoor altitude control, in and above ground effect, that accommodate the unique dynamics of the aircraft. Due to the complex airflow induced by the four interacting rotors, classical linear techniques failed to provide sufficient stability. Integral sliding mode and reinforcement learning control are presented as two design techniques for accommodating the nonlinear disturbances. The methods both result in greatly improved performance over classical control techniques.
On the compensation of dead time and zero-current crossing for a PWM-inverter-controlled AC servo drive

Conventional dead-time compensation methods, for pulsewidth-modulated (PWM) inverters, improve the current output waveforms; however, the zero-current crossing effect is still apparent. This letter proposes a new method, based on angle domain repetitive control, to reduce the distortions in the PWM inverters output waveforms caused by the dead time and the zero-crossing problem.
Sequencing technologies — the next generation

Demand has never been greater for revolutionary technologies that deliver fast, inexpensive and accurate genome information. This challenge has catalysed the development of next-generation sequencing (NGS) technologies. The inexpensive production of large volumes of sequence data is the primary advantage over conventional methods. Here, I present a technical review of template preparation, sequencing and imaging, genome alignment and assembly approaches, and recent advances in current and near-term commercially available NGS instruments. I also outline the broad range of applications for NGS technologies, in addition to providing guidelines for platform selection to address biological questions of interest.
From indoor GIS maps to path planning for autonomous wheelchairs

This work focuses on how to compute trajectories for an autonomous wheelchair based on indoor GIS maps, in particular on IndoorGML maps, which set the standard in this context. Good wheelchair trajectories are safe and comfortable for the user and the people sharing the space with him, turn gently, are high legible, and smooth (at least G2 continuos). We derive a navigation graph from a given IndoorGML map. We define and solve an optimization problem to find the desired path: given a succession of cells to traverse, the path corresponds to the best composite Bézier trajectory for the wheelchair. We discuss a related multi-objective path planning problem. Experimental results and an implementation on real robots show the planner performance.
Practical Bluetooth Traffic Sniffing: Systems and Privacy Implications

With the prevalence of personal Bluetooth devices, potential breach of user privacy has been an increasing concern. To date, sniffing Bluetooth traffic has been widely considered an extremely intricate task due to Bluetooth's indiscoverable mode, vendor-dependent adaptive hopping behavior, and the interference in the open 2.4 GHz band. In this paper, we present BlueEar -a practical Bluetooth traffic sniffer. BlueEar features a novel dual-radio architecture where two Bluetooth-compliant radios coordinate with each other on learning the hopping sequence of indiscoverable Bluetooth networks, predicting adaptive hopping behavior, and mitigating the impacts of RF interference. Experiment results show that BlueEar can maintain a packet capture rate higher than 90% consistently in real-world environments, where the target Bluetooth network exhibits diverse hopping behaviors in the presence of dynamic interference from coexisting Wi-Fi devices. In addition, we discuss the privacy implications of the BlueEar system, and present a practical countermeasure that effectively reduces the packet capture rate of the sniffer to 20%. The proposed countermeasure can be easily implemented on the Bluetooth master device while requiring no modification to slave devices like keyboards and headsets.
3D attention-driven depth acquisition for object identification

We address the problem of autonomously exploring unknown objects in a scene by consecutive depth acquisitions. The goal is to reconstruct the scene while online identifying the objects from among a large collection of 3D shapes. Fine-grained shape identification demands a meticulous series of observations attending to varying views and parts of the object of interest. Inspired by the recent success of attention-based models for 2D recognition, we develop a 3D Attention Model that selects the best views to scan from, as well as the most informative regions in each view to focus on, to achieve efficient object recognition. The region-level attention leads to focus-driven features which are quite robust against object occlusion. The attention model, trained with the 3D shape collection, encodes the temporal dependencies among consecutive views with deep recurrent networks. This facilitates order-aware view planning accounting for robot movement cost. In achieving instance identification, the shape collection is organized into a hierarchy, associated with pre-trained hierarchical classifiers. The effectiveness of our method is demonstrated on an autonomous robot (PR) that explores a scene and identifies the objects to construct a 3D scene model.
Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking

To help accelerate progress in multi-target, multi-camera tracking systems, we present (i) a new pair of precision-recall measures of performance that treats errors of all types uniformly and emphasizes correct identification over sources of error; (ii) the largest fully-annotated and calibrated data set to date with more than 2 million frames of 1080 p, 60 fps video taken by 8 cameras observing more than 2,700 identities over 85 min; and (iii) a reference software system as a comparison baseline. We show that (i) our measures properly account for bottom-line identity match performance in the multi-camera setting; (ii) our data set poses realistic challenges to current trackers; and (iii) the performance of our system is comparable to the state of the art.
A Character Model with Moral Emotions: Preliminary Evaluation

In literary and drama criticism, emotions, and moral emotions in particular, have been pointed out as one of characterizing features of stories. In this paper, we propose to model story characters as value-based emotional agents, who appraise their own and others’ actions based on their desires and values, and feel the appropriate moral emotions in response to narrative situations that challenge their goals and values. In order to validate the appropriateness of the agent model for narrative characters, we ran an experiment with human participants aimed at comparing their expectations about characters’ emotions with the predictions of the value-based model of emotional agent. The results of the experiment show that the participants’ expectations meet the predictions of the model.
Use of RFID Based Real Time Location Tracking System to Curb Diversion of Transit Goods in East Africa

In this paper, RFID based Real Time Location Tracking System (RTLS) for tracking transit goods is discussed. The system was designed to address the problem of diversions of transit wet cargo destined for hinterland countries into the local markets, so as to evade payment of duties and taxes. Transit goods are currently tracked manually and by use of manual physical escorts. Reliance on manual methods has rendered the system open to abuse by unscrupulous traders and clearing agents; at times in collusion with government officers. To be effective in tracking wet cargo, the required solution was established to be one that not only provides information in real time but also with features for intervention when cargo is perceived to be at risk. This paper discusses the design and implementation of RTLS for tracking wet cargo, and points out the strenghts, benefits, weaknesses and limitations of the solution. The effectiveness of the solution was demonstrated in addressing the weaknesses of the current manual tracking system in curbing diversion for wet cargo. In addition, the use of RTLS led to a reduction in turn around times by between thirty to forty percent compared to those which are manualy tracked. KeywordsElectronic Seals, RFID, Transit goods, Tanker Tracking, Real Time Location Tracking Systems, Cargo Tracking
Optimal Multiserver Configuration for Profit Maximization in Cloud Computing

As cloud computing becomes more and more popular, understanding the economics of cloud computing becomes critically important. To maximize the profit, a service provider should understand both service charges and business costs, and how they are determined by the characteristics of the applications and the configuration of a multiserver system. The problem of optimal multiserver configuration for profit maximization in a cloud computing environment is studied. Our pricing model takes such factors into considerations as the amount of a service, the workload of an application environment, the configuration of a multiserver system, the service-level agreement, the satisfaction of a consumer, the quality of a service, the penalty of a low-quality service, the cost of renting, the cost of energy consumption, and a service provider's margin and profit. Our approach is to treat a multiserver system as an M/M/m queuing model, such that our optimization problem can be formulated and solved analytically. Two server speed and power consumption models are considered, namely, the idle-speed model and the constant-speed model. The probability density function of the waiting time of a newly arrived service request is derived. The expected service charge to a service request is calculated. The expected net business gain in one unit of time is obtained. Numerical calculations of the optimal server size and the optimal server speed are demonstrated.
Learning and evaluating classifiers under sample selection bias

Classifier learning methods commonly assume that the training data consist of randomly drawn examples from the same distribution as the test examples about which the learned model is expected to make predictions. In many practical situations, however, this assumption is violated, in a problem known in econometrics as sample selection bias. In this paper, we formalize the sample selection bias problem in machine learning terms and study analytically and experimentally how a number of well-known classifier learning methods are affected by it. We also present a bias correction method that is particularly useful for classifier evaluation under sample selection bias.
Autonomous Navigation for Micro Aerial Vehicles in Complex GNSS-denied Environments

Micro aerial vehicles, such as multirotors, are particular well suited for the autonomous monitoring, inspection, and surveillance of buildings, e.g., for maintenance in industrial plants. Key prerequisites for the fully autonomous operation of micro aerial vehicles in restricted environments are 3D mapping, real-time pose tracking, obstacle detection, and planning of collision-free trajectories. In this article, we propose a complete navigation system with a multimodal sensor setup for omnidirectional environment perception. Measurements of a 3D laser scanner are aggregated in egocentric local multiresolution grid maps. Local maps are registered and merged to allocentric maps in which the MAV localizes. For autonomous navigation, we generate trajectories in a multi-layered approach: from mission planning over global and local trajectory planning to reactive obstacle avoidance. We evaluate our approach in a GNSS-denied indoor environment where multiple collision hazards require reliable omnidirectional perception and quick navigation reactions.
WeedMap: A Large-Scale Semantic Weed Mapping Framework Using Aerial Multispectral Imaging and Deep Neural Network for Precision Farming

The ability to automatically monitor agricultural fields is an important capability in precision farming, enabling steps towards more sustainable agriculture. Precise, high-resolution monitoring is a key prerequisite for targeted intervention and the selective application of agro-chemicals. In this paper, we present a novel weed segmentation and mapping framework that processes multispectral images obtained from an unmanned aerial vehicle (UAV) using a deep neural network (DNN). Most studies on crop/weed semantic segmentation only consider single images for processing and classification. Images taken by UAVs often cover only a few hundred square meters with either color only or color and near-infrared (NIR) channels. Although a map can be generated by processing single segmented images incrementally, this requires additional complex information fusion techniques which struggle to handle high fidelity maps due to their computational costs and problems in ensuring global consistency. Moreover, computing a single large and accurate vegetation map (e.g., crop/weed) using a DNN is non-trivial due to difficulties arising from: (1) limited ground sample distances (GSDs) in high-altitude datasets, (2) sacrificed resolution resulting from downsampling high-fidelity images, and (3) multispectral image alignment. To address these issues, we adopt a stand sliding window approach that operates on only small portions of multispectral orthomosaic maps (tiles), which are channel-wise aligned and calibrated radiometrically across the entire map. We define the tile size to be the same as that of the DNN input to avoid resolution loss. Compared to our baseline model (i.e., SegNet with 3 channel RGB inputs) yielding an area under the curve (AUC) of [background=0.607, crop=0.681, weed=0.576], our proposed model with 9 input channels achieves [0.839, 0.863, 0.782]. Additionally, we provide an extensive analysis of 20 trained models, both qualitatively and quantitatively, in order to evaluate the effects of varying input channels and tunable network hyperparameters. Furthermore, we release a large sugar beet/weed aerial dataset with expertly guided annotations for further research in the fields of remote sensing, precision agriculture, and agricultural robotics.
Similarity estimation techniques from rounding algorithms

(MATH) A locality sensitive hashing scheme is a distribution on a family $\F$ of hash functions operating on a collection of objects, such that for two objects <i>x,y</i>, <b>Pr</b><sub><i>h</i></sub>εF[<i>h</i>(<i>x</i>) = <i>h</i>(<i>y</i>)] = sim(<i>x,y</i>), where <i>sim</i>(<i>x,y</i>) ε [0,1] is some similarity function defined on the collection of objects. Such a scheme leads to a compact representation of objects so that similarity of objects can be estimated from their compact sketches, and also leads to efficient algorithms for approximate nearest neighbor search and clustering. Min-wise independent permutations provide an elegant construction of such a locality sensitive hashing scheme for a collection of subsets with the set similarity measure <i>sim</i>(<i>A,B</i>) = \frac{|A &Pgr; B|}{|A &Pgr B|}.(MATH) We show that rounding algorithms for LPs and SDPs used in the context of approximation algorithms can be viewed as locality sensitive hashing schemes for several interesting collections of objects. Based on this insight, we construct new locality sensitive hashing schemes for:<ol><li>A collection of vectors with the distance between → \over <i>u</i> and → \over <i>v</i> measured by Ø(→ \over <i>u</i>, → \over <i>v</i>)/π, where Ø(→ \over <i>u</i>, → \over <i>v</i>) is the angle between → \over <i>u</i>) and → \over <i>v</i>). This yields a sketching scheme for estimating the cosine similarity measure between two vectors, as well as a simple alternative to minwise independent permutations for estimating set similarity.</li><li>A collection of distributions on <i>n</i> points in a metric space, with distance between distributions measured by the Earth Mover Distance (<b>EMD</b>), (a popular distance measure in graphics and vision). Our hash functions map distributions to points in the metric space such that, for distributions <i>P</i> and <i>Q</i>, <b>EMD</b>(<i>P,Q</i>) &xie; <b>E</b><sub>hε\F</sub> [<i>d</i>(<i>h</i>(<i>P</i>),<i>h</i>(<i>Q</i>))] &xie; <i>O</i>(log <i>n</i> log log <i>n</i>). <b>EMD</b>(<i>P, Q</i>).</li></ol>.
Challenging Complexity of Maximum Common Subgraph Detection Algorithms: A Performance Analysis of Three Algorithms on a Wide Database of Graphs

Graphs are an extremely general and powerful data structure. In pattern recognition and computer vision, graphs are used to represent patterns to be recognized or classified. Detection of maximum common subgraph (MCS) is useful for matching, comparing and evaluate the similarity of patterns. MCS is a well known NP-complete problem for which optimal and suboptimal algorithms are known from the literature. Nevertheless, until now no effort has been done for characterizing their performance. The lack of a large database of graphs makes the task of comparing the performance of different graph matching algorithms difficult, and often the selection of an algorithm is made on the basis of a few experimental results available. In this paper, three optimal and well-known algorithms for maximum common subgraph detection are described. Moreover a large database containing various categories of pairs of graphs (e.g. random graphs, meshes, bounded valence graphs), is presented, and the performance of the three algorithms is evaluated on this database. Article Type Communicated by Submitted Revised Regular Paper U. Brandes September 2005 January 2007 D. Conte et al., Maximum Common Subgraph, JGAA, 11(1) 99–143 (2007) 100
Precise Flow-Insensitive May-Alias Analysis is NP-Hard

Determining aliases is one of the foundamental static analysis problems, in part because the precision with which this problem is solved can affect the precision of other analyses such as live variables, available expressions, and constant propagation. Previous work has investigated the complexity of flow-sensitive alias analysis. In this article we show that precise flow-insensitive may-alias analysis is NP-hard given arbitrary levels of pointers and arbitrary pointer dereferencing.
Spatial Reference in Linguistic Human-Robot Interaction: Iterative, Empirically Supported Development of a Model of Projective Relations

The aim of our research is to enable spontaneous and efficient spatial reference to objects in human-robot interaction situations. This paper presents the iterative, empirically based design of a robotic system that uses a computational model for identifying objects on the basis of a range of spatial reference systems. The efficiency of the system is evaluated by two successive empirical studies involving uninformed users. The linguistic analysis points to the striking variability in speakers’ spontaneous strategies and preferences, and it motivates a number of modifications of the computational model.
Story Generation with Crowdsourced Plot Graphs

Story generation is the problem of automatically selecting a sequence of events that meet a set of criteria and can be told as a story. Story generation is knowledge-intensive; traditional story generators rely on a priori defined domain models about fictional worlds, including characters, places, and actions that can be performed. Manually authoring the domain models is costly and thus not scalable. We present a novel class of story generation system that can generate stories in an unknown domain. Our system (a) automatically learns a domain model by crowdsourcing a corpus of narrative examples and (b) generates stories by sampling from the space defined by the domain model. A large-scale evaluation shows that stories generated by our system for a previously unknown topic are comparable in quality to simple stories authored by untrained humans.