nils-herrmann/clean_scirepeval huggingface dataset:
Performance of a Five-Phase Induction Machine With Optimized Air Gap Field Under Open Loop $V$/$f$ Control

The paper assesses the steady-state performance of a five-phase induction machine fed by a modified open loop constant volt/hertz (V/f) control method, which imposes a trapezoidal induction waveform in the air gap under varying load conditions. The trapezoidal air gap induction is achieved through the imposition of an appropriate combination of the third harmonic and fundamental stator voltages. This harmonic combination is determined from the steady-state model using a mathematical optimization procedure, which allows to obtain the optimal weighting factors for each harmonic component. The optimized reference voltages lead to a trapezoidal air gap induction, which allows a better iron utilization and higher output torque for the same rms stator current when compared to sinusoidal air gap induction. The resulting air gap induction is obtained from the induced voltage of a full pitch search coil placed in the air gap. The proposed control scheme was successfully simulated and implemented on a five-phase prototype machine running under different load conditions. Experimental and simulations results show an increase in the torque/ampere relationship for loads above 50% when compared to the conventional V/f method using only the fundamental current and air gap induction. A comparison between the simulation and experimental curves presents a very good agreement that confirms and validates the parameters and model used.
IOPT-tools — Towards cloud design automation of digital controllers with Petri nets

This paper describes the IOPT-Tools cloud-based tool-chain, offering a complete set of Petri net tools with a Web interface supporting digital controllers development. The tools include an interactive graphical Petri net editor, a model-checking subsystem composed of a state-space generator, state-space visualization and a query system, and automatic code generation tools that produce software “C” code or VHDL hardware descriptions ready to be deployed into implementation platforms. All interactive tools are executed directly in the user's Web Browser using AJAX principles, but file-storage and intensive processing operations are processed in the cloud. For example, state-space computation and the storage of the resulting data is performed in dedicated fast servers, minimizing network traffic and enabling the use of lightweight terminal equipment to access the tools, as simple as smart-phones or tablet computers. The tools are available on-line at http://gres.uninova.pt.
MPEG-21 standard: defining an open multimedia framework

The MPEG-21 project, formally called "multimedia framework", aims to understand if and how the various elements building the infrastructure for the deployment of multimedia applications fit together, and to discuss which new standards may be required, if gaps in the infrastructure exist. Once this work has been carried out, new standards will be developed for the missing elements with the involvement of other bodies, where appropriate, and finally the existing and novel standards will be integrated in the MPEG-21 multimedia framework. The MPEG-21 vision is thus to define an open multimedia framework to enable the transparent and augmented deliver)' and consumption of multimedia resources across a wide range of networks and devices used by different communities. This paper describe not only the technical objectives of the MPEG-21 standard but also its motivation and vision
An automatic path planing system for autonomous robotic vehicles

We present a reliable approach towards automatic path planing of autonomous vehicles permitting the integration of trajectory optimisation and obstacle avoidance techniques. The hierarchical structure with increasing level of accuracy allows not only to avoid local minima, but is also reliable in the sense that the best computable path will always be available. Therefore, this methodology is implementable as an autonomous tool, intended for use in both off-line mission planning and online replanning. Its application is proposed for land vehicles (ALV's), but may be extended for underwater vehicles (AUV's).<<ETX>>
Self-Adaptable and Intrusion Tolerant Certificate Authority for Mobile Ad Hoc Networks

Dynamic distributed systems like peer-to-peer systems, overlay networks and mobile ad hoc networks must execute their applications using distributed resources and in a decentralized way. The structure (composition and topology) of these systems changes frequently due to nodes joining or leaving the network, or due to mobility, faults and networks partitions. Distributed algorithms projected to this class of systems must be able to deal with the occurrence of these events and to self-adapt dynamically the application according to changes in its execution environment. In this paper, we propose a dynamic, self-adaptable and intrusion tolerant certificate authority (CA) designed to operate in mobile ad hoc networks. This CA is composed by a set of mobile devices present in the network, which forms a group of servers that supports CA functions. This architecture is based on a dynamic systems model and was conceived to manage changes in the membership of the servers group. This paper introduces an algorithmic base that allows the CA to reconfigure itself, guaranteeing the availability and the inviolability of the certification service.
An impulsive framework for the control of hybrid systems

An impulsive control formulation suitable for analyzing hybrid systems is presented. Besides a continuous evolution, the trajectory of an impulsive control system may also exhibit jumps. The jump trajectory is well characterized in this impulsive framework. These jumps can be interpreted as the discrete evolution of an hybrid system. Several examples of hybrid systems modeled in the impulsive framework are given. An impulsive formulation of a formation control problem, regarded as an hybrid system is detailed. Finally, an overview of important classes of control results available for impulsive control systems, notably, stability and optimality, attest the importance of this paradigm for the control of hybrid systems. These results are essential to investigate the properties of model predictive control schemes for hybrid systems.
PAMELA: a useful tool for the study of leaky-wave modes in strip-loaded open dielectric waveguides

A software tool is presented to help in the teaching of the electromagnetic theory of open dielectric waveguides. The study of the modal spectrum of canonical closed waveguides is a basic topic in any course of electromagnetics or microwave engineering. However, surface-wave modes and leaky-wave modes in open waveguides are not yet so well known in the academic environment. Nevertheless, this study is necessary, since novel open dielectric waveguides have been proposed for millimeter-wave and optical electronics in order to reduce the conductor losses. Therefore, the comprehension of the phenomenon of leaky-wave modes and behavior is indispensable for any future microwave engineer, who has to work with the new millimeter-wave and optical technologies. This program constitutes the first tool explicitly conceived to help the teaching of the working principles of open dielectric guides loaded with printed circuits, and the associated practical leaky-wave antennas (LWAs). The code can be a very good supplement to understand how practical leaky-wave antennas work, after a basic theoretical understanding of leaky waves has been first obtained. From the appearance of leaky-wave modes in open waveguides to the design of a backward-to-forward leaky-wave antenna, some simple and very clarifying step-by-step exercises are presented to illustrate these not-so-well-known concepts. Although the program refers to a specific type of open dielectric guides (laterally-shielded, top-open, stub-loaded, rectangular dielectric waveguides with planar perturbations), the results are applicable to any open dielectric guide, helping to teach the basic theory and topics relating to leaky- and surface-wave modes in uniform and periodic structures
Alternative resonant controller design for Uninterruptible Power Supplies (UPS)

The main objective of this paper is to present an alternative procedure to design resonant controllers for Uninterruptible Power Supplies (UPS). With this aim, a set of equations to determine controller gains is proposed based on the desired output response and the parameters of a class of UPS systems. First, the desired closed-loop poles are determined considering the Linear Matrix Inequalities (LMI) framework. Based on the characteristic polynomial representing the desired closed-loop performance and the plant parameters, algebraic conditions are derived to determine the controller gains for a range of UPS parameters and loads. Simulation and experimental results illustrate the effectiveness of the proposed methodology.
IOPT Petri net state space generation algorithm with maximal-step execution semantics

This paper presents an algorithm to efficiently generate the state-space of systems specified using the IOPT Petri-net modeling formalism. IOPT nets are a non-autonomous Petri-net class, based on Place-Transition nets with an extended set of features designed to allow the rapid prototyping and synthesis of system controllers through an existing hardware-software co-design framework. To obtain coherent and deterministic operation, IOPT nets use a maximal-step execution semantics where, in a single execution step, all enabled transitions will fire simultaneously. This fact increases the resulting state-space complexity and can cause an arc “explosion” effect. Real-world applications, with several million states, will reach a higher order of magnitude number of arcs, leading to the need for high performance state-space generator algorithms. The proposed algorithm applies a compilation approach to read a PNML file containing one IOPT model and automatically generate an optimized C program to calculate the corresponding state-space.
An underwater cooperative navigation scheme

In this article we present and discuss a cooperative localization and navigation system based on the Extended Kalman Filter specifically designed to meet the requirements of the underwater environment. These lead us to consider a SLAM-like scheme in which some other Autonomous Underwater Vehicles (AUVs) play the role of mobile environment features and range measurements are used as external data. Own “recent” navigation data and some minimal communicated data by other AUVs are used to consistently update pose estimates and error covariances of relevant vehicles. A number of simulation and experimental results are presented to illustrate the approach.
Optimization-Oriented Design of RF/Microwave Circuits Using Inverse-Linear-Input Neuro-Fuzzy-Output Space Mapping With Two Different Dimensionality Simulators

In this paper, an efficient optimization technique aligning three-dimensional (3-D) electromagnetic simulator responses with two-dimensional (2-D) electromagnetic simulator responses around space mapped solution, for the electromagnetic design of radiofrequency and microwave 3-D circuits is presented. The interface between 2-D and 3-D electromagnetic simulators is obtained from an inverse linear input space-mapping approach and an output modeling process based on a fuzzy logic technique. It is shown that the technique provides a highly accurate estimation of the 3-D design parameter space of the 3-D radiofrequency and microwave circuits to be designed, for initially fixed design specifications. Designs of first-order, second-order and sixth-order C-band 3-D evanescent rectangular waveguide bandpass filters with dielectric posts show the performance of our approach.
Automatic Ocular Disease Screening and Monitoring Using a Hybrid Cloud System

The maturity of healthcare IoT technology which connects medical devices and applications to healthcare IT systems through internet has driven the rapid growth of healthcare. In recent years, great effort has been spent to improve ocular disease screening and diagnosis using advanced image and data analysis techniques. However, the developed systems are not widely used because they are usually offline and separated from medical devices. In this paper, we introduce a platform that connects medical devices, patients, ophthalmologists, and intelligent ocular disease analysis systems through a cloud-based system. The platform is designed in a hybrid cloud pattern to offer both easy accessibility and enhanced security. The retinal fundus images and patients' personal data can be uploaded to the public cloud tier through multiple channels including retinal fundus cameras, web portals, mobile applications and APIs. The data will be transferred to the private cloud tier where automatic analysis and assessment will be performed using advanced pattern classification algorithms. Subsequently, the analysis report will be made available in the public tier so that patients can access their own report through mobile applications or web portals. Furthermore, patients with high risk of having ocular diseases will be referred to ophthalmologists. The platform helps to form an integrated ecosystem that enables an efficient and cost-effective way of ocular disease screening and monitoring, allowing early disease detection and intervention.
Automatic detection of the optic cup using vessel kinking in digital retinal fundus images

Detection of the optic cup, which is an excavation in the optic disc, is an essential step in glaucoma assessment. In digital fundus photography, which captures 2D images of the retina, identification of the optic cup can be challenging. Kinks are bendings of vessels as they traverse the optic cup boundary, and are used clinically to determine the location of the optic cup. In this paper, we propose a method to automatically detect the optic cup boundary through the use of kinks. Patches are extracted within the optic disc, from which segment-based wavelet, edge and color features are generated for vessel candidates. A SVM technique is subsequently used to classify these candidates. To detect and localize kinking, a shifting multi-scale window interval is used to probe along the vessels. The obtained kinks are combined with pallor-based information to determine the optic cup. Experiments on a sample set of data show promising results for the proposed method, close to variability between the reference and a `second observer' segmentations.
Multiple ocular diseases detection based on joint sparse multi-task learning

In this paper, we present a multiple ocular diseases detection scheme based on joint sparse multi-task learning. Glaucoma, Pathological Myopia (PM), and Age-related Macular Degeneration (AMD) are three major causes of vision impairment and blindness worldwide. The proposed joint sparse multitask learning framework aims to reconstruct a test fundus image with multiple features from as few training subjects as possible. The linear version of this problem could be casted into a multi-task joint covariate selection model, which can be very efficiently optimized via kernelizable accelerated proximal gradient method. Extensive experiments are conducted in order to validate the proposed framework on the SiMES dataset. From the Area Under Curve (AUC) results in multiple ocular diseases classification, our method is shown to outperform the state-of-the-art algorithms.
An automatic quantitative measurement method for performance assessment of retina image registration algorithms

This paper presents a novel automatic quantitative measurement method for assessment of the performance of image registration algorithms designed for registering retina fundus images. To achieve automatic quantitative measurement, we propose the use of edges and edge dissimilarity measure for determining the performance of retina image registration algorithms. Our input is the registered pair of retina fundus images obtained using any of the existing retina image registration algorithms in the literature. To compute edge dissimilarity score, we propose an edge dissimilarity measure that we called “robustified Hausdorff distance”. We show that our proposed approach is feasible as designed by drawing comparison to visual evaluation results when tested on images from the DRIVERA and G9 dataset.
Retinal vessel segmentation via deep learning network and fully-connected conditional random fields

Vessel segmentation is a key step for various medical applications. This paper introduces the deep learning architecture to improve the performance of retinal vessel segmentation. Deep learning architecture has been demonstrated having the powerful ability in automatically learning the rich hierarchical representations. In this paper, we formulate the vessel segmentation to a boundary detection problem, and utilize the fully convolutional neural networks (CNNs) to generate a vessel probability map. Our vessel probability map distinguishes the vessels and background in the inadequate contrast region, and has robustness to the pathological regions in the fundus image. Moreover, a fully-connected Conditional Random Fields (CRFs) is also employed to combine the discriminative vessel probability map and long-range interactions between pixels. Finally, a binary vessel segmentation result is obtained by our method. We show that our proposed method achieve a state-of-the-art vessel segmentation performance on the DRIVE and STARE datasets.
Geometric corner extraction in retinal fundus images

This paper presents a novel approach of finding corner features between retinal fundus images. Such images are relatively textureless and comprising uneven shades which render state-of-the-art approaches e.g., SIFT to be ineffective. Many of the detected features have low repeatability (<; 10%), especially when the viewing angle difference in the corresponding images is large. Our approach is based on the finding of blood vessels using a robust line fitting algorithm, and locating corner features based on the bends and intersections between the blood vessels. These corner features have proven to be superior to the state-of-the-art feature extraction methods (i.e. SIFT, SURF, Harris, Good Features To Track (GFTT) and FAST) with regard to repeatability and stability in our experiment. Overall in average, the approach has close to 10% more repeatable detected features than the second best in two corresponding retinal images in the experiment.
Epidermal segmentation in high-definition optical coherence tomography

Epidermis segmentation is a crucial step in many dermatological applications. Recently, high-definition optical coherence tomography (HD-OCT) has been developed and applied to imaging subsurface skin tissues. In this paper, a novel epidermis segmentation method using HD-OCT is proposed in which the epidermis is segmented by 3 steps: the weighted least square-based pre-processing, the graph-based skin surface detection and the local integral projection-based dermal-epidermal junction detection respectively. Using a dataset of five 3D volumes, we found that this method correlates well with the conventional method of manually marking out the epidermis. This method can therefore serve to effectively and rapidly delineate the epidermis for study and clinical management of skin diseases.
Automatic optic disc segmentation with peripapillary atrophy elimination

Optic disc segmentation from retinal fundus image is a fundamental but important step for automatic glaucoma diagnosis. In this paper, an optic disc segmentation method is proposed based on peripapillary atrophy elimination. The elimination is done through edge filtering, constraint elliptical Hough transform and peripapillary atrophy detection. With the elimination, edges that are likely from non-disc structures especially peripapillary atrophy are excluded to make the segmentation more accurate. The proposed method has been tested in a database of 650 images with disc boundaries marked by trained professionals manually. The experimental results by the proposed method show average m1, m2 and mVD of 10.0%, 7.4% and 4.9% respectively. It can be used to compute cup to disc ratio as well as other features for application in automatic glaucoma diagnosis systems.
Sector-based optic cup segmentation with intensity and blood vessel priors

The optic cup segmentation is critical for automated cup-to-disk ratio measurement, and hence computer-aided diagnosis of glaucoma. In this paper, we propose a novel sector-based method for optic cup segmentation. The method comprises two parts: intensity-based cup segmentation with shape constraints and blood vessel-based refinement. The initial estimation of the cup is obtained by applying a statistical deformable model on the vessel free image. At the same time, blood vessels within the optic disk are extracted, after which vessel bendings and vessel boundaries in the nasal side are located. Subsequently, these key points in the blood vessels are used to fine tune the cup. The algorithm is evaluated on 650 fundus images from the ORIGA-light database. Experimental results show that the Dice coefficient for the optic cup segmentation can be as high as 0.83, which outperforms other existing methods. The results demonstrate good potential for the proposed method to be used in automated optic cup segmentation and glaucoma diagnosis.
Towards automatic detection of age-related macular degeneration in retinal fundus images

Age-related macular degeneration (AMD) is a leading cause of blindness worldwide. The disease is highly associated with age, and becoming increasingly prevalent in our aging societies. Drusen is a pathological feature that is well-associated with AMD. In this paper, we present a method of detecting drusen in retinal fundus images. The method first determines the location of the macula, which is used as a landmark for a clinical drusen grading overlay. Subsequently, regions of drusen are identified though a maximal region-based pixel intensity approach via RGB and HSV channels. Methods of reducing the effect of retinal and choroidal vessels are also described. The system is tested on a sample set of 16 fundus images from a clinical study, with half having drusen. Experiments on the results show a sensitivity and specificity of 0.75 on the test image set.
Learning-based approach for the automatic detection of the optic disc in digital retinal fundus photographs

The optic disc is an important feature in the retina. We propose a method for the detection of the optic disc based on a supervised learning scheme. The method employs pixel and local neighbourhood features extracted from the ROI of a digital retinal fundus photograph. A support vector machine based classification mechanism is used to classify each image point as belonging to the cup and retina. The proposed method is evaluated on a sample image set of 68 retinal fundus images. The results show a high correlation (r>0.9) with the ground truth segmentation, with an overlap error of 6.02%, and found to be comparable to the inter-observer variability based on an independent second observer segmentation of the same data set.
Focal edge association to glaucoma diagnosis

Glaucoma is an optic nerve disease resulting in the loss of vision. There are two common types of glaucoma: open angle glaucoma and angle closure glaucoma. Glaucoma type classification is important in glaucoma diagnosis. Clinically, ophthalmologists examine the iridocorneal angle between iris and cornea to determine the glaucoma type as well as the degree of closure. However, manual grading of the iridocorneal angle images is subjective and often time consuming. In this paper, we propose focal edge for automated iridocorneal angle grading. The iris surface is located to determine focal region and focal edges. The association between focal edges and angle grades is built through machine learning. A modified grading system with three grades is adopted. The experimental results show that the proposed method can correctly classify 87.3% open angle and 88.4% closed angle. Moreover, it can correctly classify 75.0% grade 1 and 77.4% grade 0 for angle closure cases.