nils-herrmann/clean_scirepeval huggingface dataset:
Computation of 3-D Sensitivity Coefficients in Magnetic Induction Tomography Using Boundary Integral Equations and Radial Basis Functions

This paper presents a method for the numerical computation of 3-D sensitivity coefficients of a target object in magnetic induction tomography (MIT). The sensitivity coefficient at a point is defined as the dot product of electromagnetic fields produced by unit current flowing in the excitation and the detector coil. In this paper, the fields are governed by a set of boundary integral equations (BIEs). Numerical results demonstrate that the fields on the boundary and interior volume domain of the target can be accurately represented by radial basis functions (RBFs). The paper compares numerical solutions of the BIEs based on RBFs with analytical solutions and boundary element solutions.
Analysis of fog and smoke attenuation in a free space optical communication link under controlled laboratory conditions

In this paper, the attenuation of free space optical (FSO) communication systems operating at selected wavelengths of 0.83 μm, 1.31 μm and 1.55 μm in the controlled laboratory based fog and smoke environments are compared. The fog and smoke are generated and controlled homogeneously along the indoor atmospheric chamber of length 6 m. The experimental results from the selected wavelengths are compared for the continuous spectrum range (0.6 μm <; λ <; 1.55 μm) attenuation. The results show that, unlike the fog attenuation, the resultant smoke attenuation linearly decreases from the visible towards the near infrared (NIR) range from dense to light smoke conditions.
A proposed iterative decoder for ATSC standard

Concatenated code is widely used in multimedia systems, such as DVB-T, ISDB-T and ATSC as terrestrial digital Television broadcasting standard of Europe, Japan and America, respectively. The concatenated code usually consists of Reed-Solomon code as outer code, Convolutional code as inner coder. The byte-level interleave also used to avoid burst errors in severe wireless channel. This class of concatenated code is proved to be robust against multipath fading channel. In this paper, we review the conventional decoding algorithm for concatenated code, which is versatile to implement in hardware in decades. Then, a feasible iteration decoding method for concatenated code is proposed. The main contributions of the proposed method are (1) a novel iteration decoding method of concatenated codes, (2) a suitable modification of decoding algorithm of outer and inner codes for hardware implementation. The simulation shows that the proposed method achieves coding gain of 0.7dB upto 1.4dB in comparison with the conventional decoding method.
A single-phase unified power quality conditioner with an enhanced repetitive controller

This paper proposes a robust and flexible control strategy for a single-phase unified power quality conditioner (S-UPQC) to solve the power quality problem due to the distorted input supply voltage and the nonlinear load. In order to enhance the performance of the series and shunt active power filters (APFs) in S-UPQC, an advanced current and voltage control schemes are introduced based on the modified repetitive controller (RCs) connected with a proportional-integral (PI) controller. Unlike the conventional control scheme for the series and shunt APFs, the proposed scheme is implemented without any harmonic extractor. Furthermore, the dynamic performance is improved significantly due to the reduced delay time of the RC, and the computation time is also reduced. The effectiveness of the proposed control schemes for the S-UPQC is validated through simulation.
A Novel Analytical Model to Identify Link Quality in 802.11 Mesh Networks

The rapid growths of wireless mesh infrastructure based on IEEE 802.11 has gained more and more attention from researchers on both practical and theoretical studies. Markov chain models have been widely used in describing complex systems and can be seen as a common and effective tool to analyze behaviors of 802.11 MAC protocols in distributed multiple access environments. A novel analytical model is presented in this paper to predict quality of links in multi-hop wireless networks through the relationship between node state model and channel state model. This approach is fully investigated the CSMA/CA mechanism of 802.11 mesh network under unsaturated traffic and error-prone channel conditions. Our analysis reveals how the throughput of the 802.11-based wireless mesh networks is affected by the incoming traffic, network density, sensing range and bit error rate.
A 32 nm SoC With Dual Core ATOM Processor and RF WiFi Transceiver

An &times; 86 standard operating system compliant System-on-Chip (SoC) with a dual core ATOM processor and a custom interconnect fabric to enable modular design is presented. The 32 nm SoC includes integrated PCI-e Gen 2, DDR3, legacy I/O, voltage regulators, clock generation, power management, memory controller and RF portion of a WiFi transceiver in a 32 nm high-k/metal-gate RF CMOS process with high resistivity substrate. The integrated RF transceiver for 2.4 GHz 802.11g operation achieves a receive sensitivity of -74 dBm, -8 dBm IIP3 and a transmit output power of 20.3 dBm (-25 dB EVM) at 14% TX RF efficiency.
A new kind of training for obstetric residents: simulator training

This paper presents work resulting from a collaboration between obstetricians and researchers. It shows the benefits from the use of an instrumented childbirth simulator for the training of obstetricians and midwives. This new tool allows to surpass the constraints linked to the traditional training in a childbirth ward. This simulator training is designed to complete the traditional training used in teaching hospitals. Such a training allows residents to acquire a beginning experience before training in a childbirth ward but it also allows instructors to improve the teaching gestures without constraints. A clinical study of the forceps blades placement gesture with several residents who trained on a childbirth simulator is made. The results clearly show the progress in the obstetric gestures of all the residents who have used the simulator
An efficient multi-objective optimization approach for Online Test Paper Generation

With the rapid growth of the Internet and mobile devices, Online Test Paper Generation (Online-TPG) is a promising approach for self-assessment especially in an educational environment. Online-TPG is challenging as it is a multi-objective optimization problem that is NP-hard, and it is also required to satisfy the online generation requirement. The current techniques such as dynamic programming, tabu search, swarm intelligence and biologically inspired algorithms generally require long runtime for generating good quality test papers. In this paper, we propose an efficient multi-objective optimization approach for Online-TPG. The proposed approach is based on the Constraint-based Divide-and-Conquer (DAC) technique for constraint decomposition and multi-objective optimization. In this paper, we present the proposed DAC approach for Online-TPG and its performance evaluation. The performance results have shown that the proposed approach has outperformed other TPG techniques in terms of runtime efficiency and paper quality.
Activity Recognition Using Wrist-Worn Sensors for Human Performance Evaluation

Recent development of wearable technology has opened up great opportunities for human performance evaluation applications in various domains. In order to measure the physical activities of an individual, wrist-worn sensors embedded in smartwatches, fitness bands, and clip-on devices can be used to collect various types of data, as the subject performs regular daily activities. In this paper, we propose using the acceleration data generated by wrist-worn sensors to recognize ambulation activities for performance evaluation purposes. Twelve features are extracted from the raw accelerometer data, then feed into individual classifiers as well as their combinations for training and validation. The classifiers we consider in this paper are Naive Bayes, Support Vector Machines, Decision Tree, k-Nearest Neighbors, Multilayer Perceptron, and Random Forest, as they have been reported effective by a few previous works. We evaluate the set of selected classifiers with real-world data sets generated by three subjects, including data from both left and right wrists. The best accuracy performance is the combination of classifiers which is above 90%. The result shows that data generated by wrist-worn accelerometer sensor is sufficient for ambulation activity recognition and can be used for human performance evaluation applications.
An automatic control system for ship harbour manoeuvres using decoupling control

This paper presents a new application of the decoupling control to automatically control large ships in harbour areas. A multi-term nonlinear mathematical model is adopted to express ships' manoeuvring motions. The method for designing the automatic control system is briefly described. Computer simulation results for a very large crude carriage on a simple berthing manoeuvre are given to verify the design method. Excellent effects of the automatic control system are proven by very good simulation results of the ship motions during 180 deg. turning manoeuvres under various strong wind conditions. Simulation results also show robustness of the automatic control system against parameters' uncertainty.
A novel game-based demand side management scheme for smart grid

In order to optimize energy consumption in smart grid, demand side management has gained a lot of attention recently. While existing research works attempt to optimize energy consumption either from the view point of the power company or that of users, we investigate whether it is possible to consider both parties' interests at the same time. In this paper, we propose a novel energy price model, which is a function of the total energy consumption in the considered system. In addition, a new objective function, to optimize the difference between the value and cost of energy, is proposed. The power company sends the energy price parameter and the latest consumption summary vector information to the users sequentially. Upon receiving these information, a user can optimize his own schedule and report it to the company. The company then updates its energy price parameter before communicating with the next customers. A two-step centralized game is proposed that models this interaction between the power company and its consumers. The game aims at reducing the system peak-to-average power ratio by simultaneously optimizing users' energy schedules and lowering the overall energy consumption in the system. Through simulation results the performance of the proposed game-based demand side management technique is evaluated.
A new Mobility, Energy and Congestion Aware Routing scheme for MANETs

Mobility, energy limits and traffic congestion are the main reasons for a poor routing performance in MANETs. Inspired by those challenges, this paper proposes a new routing scheme named Mobility, Energy and Congestion Aware Routing (MECAR) to improve routing performance for MANETs. In this model, a node monitors the mobility, energy consumption and traffic congestion based on a multi-metric named AEC (constructed by Average Encounter Rate, Energy Consumption Rate, and Congestion Factor) to choose the most stable, power-rich and congestion-free path for routing. As a result, packet delivery ratio of the proposed model improves almost 20% compared to that of original AODV protocol while the number of dead nodes and routing overheads decreases significantly.
A 20-Mb/s VLC Link With a Polymer LED and a Multilayer Perceptron Equalizer

This letter experimentally demonstrates a visible light communication system using a 350-kHz polymer light-emitting diode operating at a total bit rate of 19 Mb/s with a bit error rate (BER) of 10-6 and 20 Mb/s at the forward error correction limit for the first time. This represents a remarkable net data rate gain of ~55 times. The modulation format adopted is ON-OFF keying in conjunction with an artificial neural network classifier implemented as an equalizer. The number of neurons used in the experiment is varied from the set N = {5, 10, 20, 30, 40} with 40 neurons offering the best performance at 19 Mb/s and the BER of 10-6.
Assisting the visually impaired using depth inference on mobile devices via stereo matching

A novel system for assisting the visually impaired, built upon the platform of a mobile device, is proposed. The system uses depth inference and a depth-to-sound mapping to inform the user of the surroundings via sound. Stereo matching is proposed as the appropriate tool for depth inference, and stereo matching approaches are assessed for their potential for real-time performance on a mobile device and their inference quality. A novel depth-to-sound mapping is proposed that can be adjusted by the user through the touch interface of the mobile device. This mapping allows the user to define the field of view and the location of inference. A smart phone “app” and demo will be produced. The system demonstrates the viability and great benefit of depth inference on mobile devices for assisting the visually impaired.
Demonstration of a low loss and ultra highly nonlinear AsSe suspended-core chalcogenide fiber

We report a suspended-core chalcogenide fiber with a record Kerr nonlinearity of 31 300 W<sup>−1</sup> km<sup>−1</sup> and an attenuation of 4.6 dB/m. The dispersion, the effective area and the number of modes propagating in the fiber are investigated.
Design and simulation of PI-type control for the Buck Boost converter

Industry development and quality assurance in this field always addresses energy requirements that are likely to grow in the future. Among renewable energy sources, it is impossible not to mention the powerful increasing development of wind and sun energy systems. These sources of energy bring a large amount of electricity to almost all European countries. It is therefore possible to say that these sources of energy play a significant role in energy generation and storage systems. However, uncertainty factors of renewable energy always set strict requirements regarding power generation, as well as to maintain the optimal power source for the user. To meet the power requirements of these types of energy, the present paper analyzes the DC / DC converter and focuses on the PI controller for the Buck Boost converter. This study was simulated using Matlab / Simulink.
A horizontal vibration isolator with electromagnetic planar actuator using multi hall-effect sensor network

This paper presents a horizontal vibration isolator with electromagnetic planar actuator using multi hall-effect sensor network. The horizontal vibration isolator under study consists of two main parts: a passive vibration isolator and an electromagnetic planar actuator using multi hall-effect sensor network. A model of a planar actuator consists of 1D Halbach magnet array and a set of 3 phase coils is derived based on DQ decomposition and the muti hall-effect sensor is built to achieve the relation position of the magnet array and coil. Simulations and experiment model are performed to verify horizontal disturbance rejection of the vibration isolator.
An Application of Neural Networks in the Connection Admission Control of ATM Networks

The connection admission control (CAC) in ATM networks is a flow controlling function that decides to allow or not a new connection joining in the network. This decision is usually based on the status of the current ATM network as its available resources, flow parameters and the quality of registered service (QoS) of new connections joining in the network as well as existing connections. This article proposes a CAC model in which neural network is used as a tool to maximize the number of admitted connections, the "profit" derived from accepted connections (thought from their QoS) or simply the used bandwidth.
A Novel Multi-Axis Compliant Prosthetic Ankle Foot to Support the Rehabilitation of Amputees

Passive compliant prosthetic ankles have the advantage of energy saving, light weight, simple structure and competitive cost compared to powered devices. Despite of the variety of commercial products, these designs do not provide adequate degree-of-freedom during amputees' movement. It hampers them from walking comfortably especially on undulating and rough terrains. This paper presents a novel design of a multiaxis fully compliant prosthetic ankle. Finite element analyses are carried out to evaluate the mechanical behaviors of the design obtained by an optimization procedure. The designed prosthesis not only has a capability of bending and compression to store elastic energy and release it to assist in forward propulsion of the body but can also facilitate eversion motion when persons with disability move on rough road by integrating a circular flexure hinge. These characteristics make it more versatile and reduce the impact force on residual limbs.
An adaptive visual attentive tracker for human communicational behaviors using HMM-based TD learning with new State distinction capability

To develop a nonverbal communication channel between an operator and a system, we built a tracking system called the Adaptive Visual Attentive Tracker (AVAT) to track and zoom in to the operator's behavioral sequence which represents his/her intention. In our system, hidden Markov models (HMMs) first roughly model the gesture pattern. Then, the state transition probabilities in HMMs are used to assign as the rewards in temporal difference (TD) learning. Later, the TD learning method is utilized to adjust the action model of the tracker for its situated behaviors in the tracking task. Identification of the hand sign gesture context through wavelet analysis autonomously provides a reward value for optimizing AVAT's action patterns. Experimental results of tracking the operator's hand sign action sequences during her natural walking motion with higher accuracy are shown which demonstrate the effectiveness of the proposed HMM-based TD learning algorithm of AVAT. During TD learning experiments, the exploring randomly chosen actions sometimes exceed the predefined state area, and thus involuntarily enlarge the domain of states. We describe a method utilizing HMMs with continuous observation distribution to detect whether the state would be split to make a new state. The generation of new states brings the ability of enlarging the predefined area of states.
An Efficient Multilevel Healthy Cloud System Using Spark for Smart Clothes

Nowadays, we can find part of people into the suboptimal health status. However, most of them have not enough time for health examinations regularly. Smart clothes which are wearable and convenient devices for monitoring physiological signals solve this problem. In this study, we develop an efficient multilevel healthy cloud system to analyzing data collected from CGU smart clothes. The cloud system classifies the raw data into three types: normal data, continuous data, and multimedia data. After that, the cloud system analyzes data and predict diseases using Apache Spark. And then, the system will transfer final results to smartphones. The experimental result shows that the cloud system achieves very high-speed performance. Moreover, the system has friendly health information presentation and effective authorization process to help doctors and patients contact each other.
Bounded Self-Weights Estimation Method for Non-Local Means Image Denoising Using Minimax Estimators

A non-local means (NLM) filter is a weighted average of a large number of non-local pixels with various image intensity values. The NLM filters have been shown to have powerful denoising performance, excellent detail preservation by averaging many noisy pixels, and using appropriate values for the weights, respectively. The NLM weights between two different pixels are determined based on the similarities between two patches that surround these pixels and a smoothing parameter. Another important factor that influences the denoising performance is the self-weight values for the same pixel. The recently introduced local James-Stein type center pixel weight estimation method (LJS) outperforms other existing methods when determining the contribution of the center pixels in the NLM filter. However, the LJS method may result in excessively large self-weight estimates since no upper bound is assumed, and the method uses a relatively large local area for estimating the self-weights, which may lead to a strong bias. In this paper, we investigated these issues in the LJS method, and then propose a novel local self-weight estimation methods using direct bounds (LMM-DB) and reparametrization (LMM-RP) based on the Baranchik’s minimax estimator. Both the LMM-DB and LMM-RP methods were evaluated using a wide range of natural images and a clinical MRI image together with the various levels of additive Gaussian noise. Our proposed parameter selection methods yielded an improved bias-variance trade-off, a higher peak signal-to-noise (PSNR) ratio, and fewer visual artifacts when compared with the results of the classical NLM and LJS methods. Our proposed methods also provide a heuristic way to select a suitable global smoothing parameters that can yield PSNR values that are close to the optimal values.
A memoryless state observer for discrete time-delay systems

This paper presents a reduced-order memoryless state observer with a /spl gamma/-stability margin (0</spl gamma/<1) for linear discrete-time systems with multiple delays in both the state and control vectors. The dynamics of the observer are derived from a set of unstable and/or poorly damped eigenvalues of the system. A simple and systematic design method is presented. A numerical example is given to illustrate the properties of the new observer and its design method.
A novel approach for phishing detection using URL-based heuristic

Together with the growth of e-commerce transaction, Phishing - the act of stealing personal information - rises in quantity and quality. The phishers try to make fake-sites look similar to legitimate sites in terms of interface and uniform resource locator (URL) address. Therefore, the numbers of victim have been increasing due to inefficient methods using blacklist to detect phishing. This paper proposes a new phishing detection approach based on the features of URL. Specifically, the proposed method focuses on the similarity of phishing site's URL and legitimate site's URL. In addition, the ranking of site is also considered as an important factor to decide whether the site is a phishing site. The proposed technique is evaluated with a dataset of 11,660 phishing sites and 5,000 legitimate sites. The results show that the technique can detect over 97% phishing sites.
Bandwidth allocation based on declared UPC parameters in ATM networks

We present a new method to calculate the required bandwidth for ATM connections. We first develop analytical models for bandwidth allocation of constant bit rate (CBR) and variable bit rate (VBR) services based on the declared usage parameter control (UPC) parameters. Based on the analytical models, we derive the closed form expressions of the required bandwidth for both service classes. Numerical analysis is given and it shows a close relationship between the quality of service (QoS) parameters (cell loss and delay parameters) and network's resource (link capacity and buffer size). The closed form expressions can be used for simple but efficient bandwidth allocation scheme of call admission control (CAC) function.