Miracle-dz/UniFAR-FTUs huggingface dataset:
Effective Prediction in Amazon Web Service based Clustered Data using Artificial Neural Networks

Now a days, there are number of cab services providers which are confusing the client with number of similar kind of service options. That's why there is a need of selecting a quality service from a set of given services. It motivates us to design an approach which is efficient in providing the effective service after the data analysis of real time data developed and collected through Amazon Web Services. This paper aims at analyzing a mathematical model that is used to predict the most efficient output of a dataset of a model using Artificial Neural Network. The proposed mathematical model is made and applied on a database of real time cab services developed using Amazon Web Services to predict the effective web service. We have analyzed the real time dataset using Rapidminer. The Analysis is done on the bases of the threshold and root mean square error values obtained. We are proposing the methodology which is highly competent and accurate in obtaining the results.
PRAGMA Cloud Scheduler: Improving Usability of the PRAGMA Cloud Testbed

The Pacific Rim Application and Grid Middleware Assembly (PRAGMA) is a community of research scientists and institutions from around the Pacific Rim that collaborate together to enable scientific expeditions in areas such as biodiversity distribution and lake ecology. Previously, a web-based cloud scheduler reservation system was proposed to enable users to run application experiments using virtual clusters on a persistent distributed international infrastructure, called the PRAGMA Cloud Testbed. As the testbed has expanded to a broader group of users and more complex resources, the existing PRAGMA cloud scheduler became not as user-friendly as it was with the initial simple usage scenario, and insufficient for the administrators to handle anticipated more complicated use cases. This paper presents a redesign of the user interface component of this PRAGMA cloud scheduler and its additional features in order to improve the usability of the testbed for a broad range of users so that they can easily create and manage virtual clusters and run experiments for their long tail of science. This scheduler has a client-server architecture that requires minimal installation and management effort for participating testbed sites. We extended the existing PRAGMA technologies that the scheduler was built upon to facilitate automatic access to testbed resources for heterogeneous sites and applications. These extensions include unified management of virtual cluster images and expanded virtual cluster porting to a variety of cloud hosting environments. The new user interface was designed and built using node.js, React, SASS and Google Maps API and is based on principles of human computer interaction and the SOLID principles of object-oriented design. As a result, we have the new PRAGMA cloud scheduler that is more user-friendly, understandable, flexible and maintainable.
Kinematic Analysis of Needle-like Overtube Supporting Robot

This paper presents the design and structure simulation of needle-like overtube shape locking robot. This robot is designed for applying to natural orifice transluminal endoscopic surgery, or NOTES, and single port surgery, or SPS. It consists of three main parts; needle, snare, and remote center of motion (RCM) mechanism with three degrees of freedom. Using the RCM mechanism, a needle with a 3mm of outer diameter can locate at the point of fulcrum from out of the abdomen. Once the needle reaches the endoscope, a snare comes out of the end of the needle and grasps the endoscope. This method can be used to specify the position of the endoscope in the abdomen cavity. The length of needles and the design requirements of the RCM mechanism were designed in consideration of the insertion into the abdomen cavity and the range of motion of the surgical tool within the abdomen cavity. In order to analysis workspace, kinematic analysis and simulation was conducted.
Networking-Enabling Enhancement for a Swarm of COTS Drones

This paper presents the augmentation of a commercial-of-the-self (COTS) multi-rotor unmanned aerial system (UAS) to extend its capabilities by introducing increased on-board processing power. The option to process sensor information on-board reduces the required bandwidth by only communicating relevant or important information and therefore increases the level of UAS autonomy. It also enables adaptive reactive abilities for a group of UAS executing a coordinated, collaborative or cooperative mission. This paper describes an effort to integrate a small board computer, more specifically, an Odroid C2 with the COTS 3DR Solo drone, and reviews the achieved enhanced capabilities of such a system. This is an extension of a traditional autopilot, as such, the original Ardupilot hardware and software is not modified in this work.
Impact of Permanent Magnets Shape and Arrangement for Selected Parameters in Coreless Axial Flux Generator

This paper presents analysis of 3-phase Axial Flux Coreless Generator with one stator with non-overlapping windings and two rotor discs with 28 Permanent Magnets on each. The author focuses on comparison of generator efficiency and Electromotive Force shape depending on shape and arrangement and volume of the Permanent Magnets using the field calculation. To show the impact of Permanent Magnets for work of AFPM Generator the author used simulation Program ANSYS Maxwell. Simulation results show the possibility of increasing the efficiency of this kind of machines exchanging only the Permanent Magnets while maintaining the economic aspect and taking into account the attractive force between the rotor discs.
Double Density Wavelet with Fast Bilateral Filter based Image Denoising for WMSN

Removal of Gaussian noise from the sensor imageduring acquisition is such a difficult task in the field of Wireless Multimedia Sensor Network (WMSN). There are several image denoising algorithms proposed so far in the field of WMSN to reduce the impact of noise over images. Out of various denoising algorithms, wavelets have shown superior performance as image denoising technique. However, the properties of wavelets may affect the denoised output significantly for different set of images. In order to avoid this, an attempt has been made in this paper to reduce the Gaussian noise during image acquisition by utilizing the double density dual tree wavelet transform. Further, the quality of the image can be enhanced by appending the Fast Bilateral Filter (FBF) with the double density wavelets. To understand the behavior of the double density wavelet based image denoising technique, it is simulated and compared with the existing dual tree complex wavelet transform based denoising technique for different noise levels by using MATLAB simulation
50 Years of Permutation, Spatial and Index Modulation: From Classic RF to Visible Light Communications and Data Storage

In this treatise, we provide an interdisciplinary survey on spatial modulation (SM), where multiple-input multiple-output microwave and visible light, as well as single and multicarrier communications are considered. Specifically, we first review the permutation modulation (PM) concept, which was originally proposed by Slepian in 1965. The PM concept has been applied to a wide range of applications, including wired and wireless communications and data storage. By introducing a 3-D signal representation, which consists of spatial, temporal, and frequency axes, the hybrid PM concept is shown to be equivalent to the recently proposed SM family. In contrast to other survey papers, this treatise aims for celebrating the hitherto overlooked studies, including papers and patents that date back to the 1960s, before the invention of SM. We also provide simulation results that demonstrate the pros and cons of PM-aided low-complexity schemes over conventional multiplexing schemes.
Bone Conduction Auxiliary and Tactics for Man Machine Interface for Hearing Impaired Users

Traditional hearing aid devices do not correct the hearing losses but amplifies the sound, Sensorineural hearing losses and which do not involve any damage to the cochlea can be cured using devices that amplify sound or by making it louder so that it can reach the inner ear but problems like abnormal spectral and temporal processing are complex and would not be solved by amplification rather use of bone conduction can be useful, the paper presents a report on bone-conduction devices (BCDs) that enables people with impaired hearing to gain the ability to hear sounds without the usage of their eardrums. Bone conduction is the transmission of sound vibrations directly to the inner ear (cochlea). There are different types of bone conduction devices based on different principles that can be used by patients and this paper gives a review of BCDs and also proposes a methodology to improve the existing bone conduction devices by improving the speech intelligibility with help of super direction beam former.
Sideslip Angle Estimation of Ground Vehicles in a Finite Frequency Domain Through H∞ Approach

This paper studies the sideslip angle estimation problem of a ground vehicle in a finite frequency (FF) domain. The main objective is to estimate the sideslip angle using the yaw rate measurements. Filter design conditions are developed based on the uncertain lateral dynamics model, and time domain interpretations of the kalman Yakubovich Popov lemma (GKYP lemma). The designed filter ensures that the estimation error system is stable and has a prescribed H attenuation level, over a specified FF domain of the vehicle steering angle. Simulation results are presented to demonstrate the effectiveness of the proposed approach.
H2 Controller Design for Flexible and Rigid Bodied Railway Vehicles

In this paper, a six degrees-of freedom half-car model for a high-speed railway train is used to study the flexible and rigid car body dynamics under the presence of random track irregularities. A robust controller design to optimize the vertical ride quality is assessed by reducing the H2 norms of the wheel-rail forces while reducing the flexible vibrations in the carbody so as to improve the ride comfort of the railway vehicle. The random vibration responses of the coupled vehicletrack model based on simulation studies in MATLAB software, showed that the structural coupling can be obstructed by attenuating the vibrations of both the flexible modes and rigid modes which are suppressed effectively by the active suspension design.
Comparative Study on Defect Prediction Algorithms of Supervised Learning Software Based on Imbalanced Classification Data Sets

With the development of high complexity and high integration of software systems, the quality of software has gradually received widespread attention in scientific research and engineering. Software defect prediction technology plays an important role in improving software quality, reducing software development time, and reducing testing expenses. It has also become one of the hot issues in the field of software engineering research in recent years. As an important learning method in machine learning, supervised learning is widely used in the classification and regression prediction with annotation data because of its high accuracy, mature theory, and simple calculation. However, the imbalanced classification problem of data sets is common in practical applications and seriously affects the performance of learning algorithm. This paper analyzes the characteristics of software forecasting technology from the perspective of supervised learning, and performs balance like processing on imbalanced classification NASA data sets (JM1, KC3, MC1). NASA data sets after application processing are used to conduct experiments on LWL, C4.5, Random forest, Bagging, Bayesian Belief Network, Multilayer Feed forward Neural Network, SVM and NB-K algorithms, and experimental data is analyzed and evaluated.
In the World, but Not of the World

Learning to be in this world and not of this world is not easy, and as we discuss this subject today, I hope that you will listen with the Spirit to the things the Spirit wants to teach you and write down an action list that the Lord gives you that is personal and directed to you. That should be part of our learning in any situation of this kind. In my current calling as a counselor in the Young Women general presidency, I hear daily of the challenges that our youth and their parents are facing in living gospel standards. And it might be easy for us to get discouraged about this and the conditions we live in, but I also hear of much good that is happening with our youth in these days. President Hinckley has complimented them twice in the last two conferences. In this most recent conference, he said, "We commend our wonderful youth who for the most part stand up to the evils of the world, who push these evils aside and live lives pleasing to the Lord" ("Closing Remarks," Ensign, May 2005, 102).
Analytics of Deep Neural Network-Based Background Subtraction

Deep neural network-based (DNN-based) background subtraction has demonstrated excellent performance for moving object detection. The DNN-based background subtraction automatically learns the background features from training images and outperforms conventional background modeling based on handcraft features. However, previous works fail to detail why DNNs work well for change detection. This discussion helps to understand the potential of DNNs in background subtraction and to improve DNNs. In this paper, we observe feature maps in all layers of a DNN used in our investigation directly. The DNN provides feature maps with the same resolution as that of the input image. These feature maps help to analyze DNN behaviors because feature maps and the input image can be simultaneously compared. Furthermore, we analyzed important filters for the detection accuracy by removing specific filters from the trained DNN. From the experiments, we found that the DNN consists of subtraction operations in convolutional layers and thresholding operations in bias layers and scene-specific filters are generated to suppress false positives from dynamic backgrounds. In addition, we discuss the characteristics and issues of the DNN based on our observation.
On the Way to Close the Loop in Information Logistics: Data from the Patient — Value for the Patient

Summary Objective: To summarize recent research and to propose a selection of best papers published in 2017 in the field of Clinical Information Systems (CIS). Method: Each year a systematic process is carried out to retrieve articles and to select a set of best papers for the CIS section of the International Medical Informatics Association (IMIA) Yearbook of Medical Informatics. The query aiming at identifying relevant publications in the field of CIS was refined by the section editors during the last years. For three years now, the query is stable. It comprises search terms from the Medical Subject Headings (MeSH) thesaurus as well as additional free text search terms from PubMed and Web of Science®. The retrieved articles were categorized in a multi-pass review carried out by the two section editors. The final selection of candidate papers was then peer-reviewed by Yearbook editors and external reviewers. Based on the review results, the best papers were then selected by the IMIA Yearbook editorial board. Text mining, and term co-occurrence mapping techniques were used to get an overview on the content of the retrieved articles. Results: The query was carried out in mid-January 2018, yielding a consolidated result set of 2,255 articles which had been published in 939 different journals. Out of them, 15 papers were nominated as candidate best papers and four of them were finally selected as best papers in the CIS section. Again, the content analysis of the articles revealed the broad spectrum of topics which is covered by CIS research. Conclusions: Modern clinical information systems serve as backbone for a very complex, trans-institutional information logistics process. Data that is produced by, documented in, shared via, organized in, presented by, and stored within clinical information systems is more and more reused for multiple purposes. We found a lot of examples showing the benefits of such data reuse with various novel approaches implemented to tackle the challenges of this process. We also found that the patient moves in the focus of interest of CIS research. So the loop of information logistics begins to close: data from the patients is used to produce value for the patients.
Improved Multiplication Algorithm by Clearing Leading Zeros of Binary Numbers based on Big Data Analysis

System performance plays an important role in the era of big data. As such, the multiplication algorithm and hardware have evolved to improve system performance which can support a fast big data analysis engine. Several variations on conventional Booth multiplication algorithm have improved performance. However, we have observed that the existing multiplication algorithms repeating addition and shift operations conduct unnecessary iteration due to leading zeros in binary numbers. We have found that we can reduce the number of iterations by clearing unnecessary leading zeros in the multiplier. Thus, we propose two approaches to clear unnecessary leading Os: (1) pre-front-based: pre-clearing leading zeros with the front-based multiplication algorithm and (2) post-booth: post-clearing leading zeros with the rear-based multiplication algorithm using Booth algorithm. We evaluate performance with the data extracted from sparse matrices in UF Matrix Collection. Our algorithms outperform conventional multiplication algorithms by using a minimum number of necessary binary digits in the multiplier. The algorithms can be applied directly below the level of multiplication instructions with a minimal change to deal with big data ingestion.
ANALYSIS AND DETECTION OF TRANSIENTS IN ISLANDED MICRO-GRIDS USING WAVELET TRANSFORMATION

New challenges are presented for reliable operation of the power system due to increasing penetration of distributed generations (DGs). The low inertia of the generator and their power electronics interface decoupled from the grid makes it much more difficult to maintain system stability, especially for islanded micro-grids. To ensure the power quality and system reliability, the accurate and effective detection of transient signals is required. This paper utilizes wavelet transformation to detect and classify the transient signals in islanded micro-grids. The results show that the disturbances are successfully identified by applying wavelet transform in both simulation and experimental conditions. The classification of disturbances was also investigated. Coefficient spectrums and histograms are plotted to identify different disturbances in the micro-grid.
Autonomous Vehicle Steering Wheel Estimation from a Video using Multichannel Convolutional Neural Networks

The navigation technology in autonomous vehicles is an artificial intelligence application which remains unsolved and has been significantly explored by the automotive and technological industries. Many image processing and computer vision techniques allow significant improvements on such recent technologies. Using this motivation as a basis, this work proposes a novel methodology based on Multichannel Convolutional Neural Networks (M-CNN) capable of estimating the steering angle of an autonomous vehicle, having as only input images captured by a camera attached to the vehicle’s frontal area. We propose five Convolutional Neural Network architectures: 1-channel, 2-channel and 3-channel in the convolution step. Based on the performed tests using a public video dataset, it is presented a quantitative comparison between the proposed models.
Fiber-Optic Vector Magnetometer Based on Magnetic Fluid and Fiber Bragg Grating Written on a Multi-Clad Fiber

The magnetic fluid is used to integrate with a special fiber Bragg grating (FBG) inscribed over the core and innermost cladding of a multi-clad fiber, which acts as a bridge between magnetic field measurement and FBG. The FBG is etched by hydrofluoric acid and packaged with a magnetic fluid inside a capillary. The reflection of the sensor provides two resonance modes of which the cladding mode intensity shows a great response to both the magnetic field intensity and the direction. Meanwhile, the fundamental core mode intensity is quiet stable, and thus the proposed sensor clearly demonstrates the potential to simultaneous measurement of magnetic field and temperature.
A Ti/AlOx/TaOx/Pt Analog Synapse for Memristive Neural Network

Electronic synapse with precise analog weight tuning ability and long-term retention is the vital device foundation of memristor-based neuromorphic computing systems. In this letter, we propose a Ti/AlOx/TaOx/Pt memristor as an analog synapse for memristive neural network applications. The device shows high uniformity, excellent analog switching behaviors (up to 200 resistance states under triangle pulses) and excellent long-term retention of each state (up to 30 000 s). Furthermore, the precise modulation of the device resistance state (with 1.7% tolerance) can also be achieved by a finer writing program within 50 cycles.
Decentralized Multi-Area Look-Ahead Dispatch for Cross-Regional Renewable Accomodation

High variability of renewable generations poses significant challenges to the secure and economic operation of power systems, especially in terms of ramp scarcity. This paper introduces a multi-area look-ahead coordination framework for real-time dispatch, which guarantees that each region has sufficient ramping capability through a cross-area sharing of ramp resources. This approach allows for a more reliable and economic operation with high renewable variabilities. In the proposed framework, each area dispatch center conducts its own look-ahead optimization with exchanged information about the boundary states and marginal costs of their neighboring areas. Power exchanges are scheduled to deliver ramping supports to areas with large net load variations in the upcoming time steps. A dynamic multiplier-based algorithm is employed for solving the multi-area look-ahead model, which is fully decentralized and thus preserves autonomy for each area. The dynamic multiplier-based algorithm presents the advantage of accelerated computation and better robustness when dealing with stringent ramp constraints. Numerical examples in a 6-bus system illustrate the benefits of conducting multi-area look-ahead coordination.
Infrastructure-Based Vehicle Maneuver Estimation with Intersection-Specific Models

Advanced Driver Assistance Systems have the potential of making traffic safer, more efficient and convenient. At urban intersections the conflicting intentions of different traffic participants frequently lead to accidents. In order to avoid dangerous situations in such a scenario an ADAS requires an accurate estimation of future driving maneuvers executed by other vehicles. In this work we present a framework that estimates the driving maneuver of vehicles approaching at intersections where the vehicle state can only be observed externally. The high variance in driver behavior that arises from different intersection layouts is met with intersection-specific models that are computed locally. The performance of the estimator and the potential of intersection-specific models are validated with data recorded from real intersections.