Miracle-dz/UniFAR-FTUs huggingface dataset:
Low-complexity block turbo code decoding for soft-decision error correction

As the throughput needed for communication systems and storage devices increases, high-performance forward error correction (FEC), especially soft-decision (SD) based technique, becomes essential. In particular, block turbo codes (BTCs) and low-density parity check (LDPC) codes are considered as candidate FEC codes for the next generation systems, such as beyond-100 Gbps optical networks and under-20 nm NAND flash memory devices, which require capacity-approaching performance and very low error floor. The BTCs have definite strengths in diversity and encoding complexity because they generally employ a two-dimensional structure, which enables sub-frame level decoding for the row or column code-words. This sub-frame level decoding gives a strong advantage for parallel processing. The BTC decoding throughput can be improved by applying a low-complexity algorithm to the small level decoding or by running multiple sub-frame decoding modules simultaneously. In this dissertation, we develop high-throughput BTC decoding software that pursuits these advantages. The first part of this dissertation is devoted to finding efficient test patterns in the Chase-Pyndiah algorithm. Although the complexity of this algorithm linearly increases according to the number of the test patterns, it naively considers all possible patterns containing least reliable positions. As a result, consideration of one more position nearly doubles the complexity. To solve this issue, we first introduce a new position selection criterion that excludes some of the selected ones having a relatively large reliability. This technique excludes the selection of sufficiently reliable
Scalable and high-performance mpi design for very large infiniband clusters

In the past decade, rapid advances have taken place in the field of computer and network design enabling us to connect thousands of computers together to form high-performance clusters. These clusters are used to solve computationally challenging scientific problems. The Message Passing Interface (MPI) is a popular model to write applications for these clusters. There are a vast array of scientific applications which use MPI on clusters. As the applications operate on larger and more complex data, the size of the compute clusters is scaling higher and higher. Thus, in order to enable the best performance to these scientific applications, it is very critical for the design of the MPI libraries be extremely scalable and high-performance. InfiniBand is a cluster interconnect which is based on open-standards and gaining rapid acceptance. This dissertation presents novel designs based on the new features offered by InfiniBand, in order to design scalable and high-performance MPI libraries for large-scale clusters with tens-of-thousands of nodes. Methods developed in this dissertation have been applied towards reduction in overall resource consumption, increased overlap of computation and communication, improved performance of collective operations and finally designing application-level benchmarks to make efficient use of modern networking technology. Software developed as a part of this dissertation is available in MVAPICH, which is a popular open-source implementation of MPI over InfiniBand and is used by several hundred top computing sites all around the world.
An Emerging Technology: Augmented Reality to Promote Learning

Abstract—This study aims to determine the benefits and limitations of AR applications in computer hardware education, when used as a supplementary technology. The study was designed according to pre-test post-test research model with experimental and control groups. The teacher candidates studied the 3D visuals both in and out of the classroom by scanning the markers in their books. In addition, they watched educational animations and videos on required subjects. As a result, in this study where Augmented Reality applications assisted with reflective learning skills were implemented in a computer hardware course, teacher candidates generally reported positive views and suggestions regarding AR applications.
A comparative study of quadmesh compression for Touma-Gotsman and Spirale Reversi schemes.

A 3D mesh is m ade up of polygonal faces. A com plete description of such a mesh is provided by its connectivity (adjacencies of polygons) and geom etry (vertex coor­ dinates). For m ost practical work, the meshes used are either triangu lar (all face's are triangles) or quadrilateral (all faces are quadrilaterals) meshes (quadmeshos. for short). Recently, there has been much work on compressing th e connectivity inform a­ tion perta in ing to a mesh. For quadm esh compression, existing algorithm s triangu late th e quadm esh first, and then apply triangle mesh compression techniques as previ­ ous researches on mesh compression were m ostly focused on triangle meshes. To avoid the additional triangulation step, we propose two direct techniques to compress and decom press th e connectivity of quadm eshes in linear tim e. In this thesis, we will describe how' to extend two well-known triangle, mesh compression algorithm s to quadm esh compression, and how to apply encoding schemes for them . A com parison of the two algorithm s for quadm esh com pression is also given. Keyword: quadm esh, quadrilateral mesh, mesh compression, Edgebreaker. Spirale Reversi, connectivity encoding, linear encoding
CityGML: An Open Standard for 3D City Models

Google Earth and Google’s recent acquisition of SketchUp has sparked tremendous interest in 3D city modeling. Microsoft’s recent acquisition of the 3D visualization company GeoTango suggests that Microsoft’s Virtual Earth, too, will soon be providing 3D capabilities. This article discusses a proposed standard (CityGML) as a common information model for representing 3D urban objects.
Brain - Computer Interface for Communication via Record Electrophysiological Signals

The brain-computer interface (BCI) aim to use Electroencephalography (EEG) activity or other electrophysiological measures of brain functions as new non-muscular channels for communication with different smart devices for disabled persons. For connection with different smart devices was used recorded with experimental setup electrophysiological signals for execution of five different mental tasks. BCI records brain signals and processes them to produce device commands. This signal processing has two stages. The first stage is feature extraction and calculation of the values of specific features of the signals. The second stage is a translation algorithm that translates these features into device commands. Processed signals after noise filtering, clustering and classification with Bayesian Network classifier and pair-wise classifier was estimated and put into brain-computer interface for connection with smart devices.
Development of smart, compact fusion diagnostics using field-programmable gate arrays

Abstract: Fusion research requires high quality diagnostics to understand the complex physical processes involved. Traditional analogue systems are complex, large and expensive, and expansion of diagnostic capabilities is often impossible without building a completely new system at considerable expense. Field-programmable gate array (FPGA) technology can provide a solution to this problem. By implementing complex functionality and digital signal processing on an FPGA chip, diagnostic hardware can be greatly simpliﬁed and compacted. In this thesis we describe the enhancements of two diagnostics for the MAST-Upgrade tokamak using FPGA technology. Firstly, the design of the back end electronics for the new divertor bolometer is described. Results of tests of the new electronics at a number of sites, including lab-based testing and tokamak installations, are also presented. We demonstrate the correct functionality of the electronics and illustrate a number of important eﬀects which must be taken into account when interpreting bolometer data on MAST-U. Secondly, we describe the new control and acquisition electronics developed for the MAST-U divertor Langmuir probe diagnostic. Much of the analogue control circuitry of the previous system has been upgraded to a digital implementation on an FPGA, which results in a signiﬁcantly more compact and cost eﬀective design. Given that MAST-Upgrade will feature around 850 Langmuir probes, these improvements are extremely important to keep the diagnostic manageable. Again, results are presented from the testing of the system at several sites, which both demonstrate the correct functionality of the new system and provide information on the diagnostic behaviour which needs to be accounted for when interpreting the probe data during MAST-U experiments.
Hierarchical Reinforcement Learning for Sequencing Behaviors

Recent literature in the robot learning community has focused on learning robot skills that abstract out lower-level details of robot control, such as Dynamic Movement Primitives (DMPs), the options framework in hierarchical RL, and subtask policies. To fully leverage the efficacy of these macro actions, it is necessary to then sequence these primitives to achieve a given task. Our objective is to jointly learn a set of robot skills and a sequence of these learnt skills to accomplish a given task. We consider the task of navigating a robot across various environments using visual input, maximizing the distance traveled through the environment while avoiding static obstacles. Traditional planning methods to solve this problem rely on hand-crafted state representations and heuristics for planning, and often fail to generalize. In contrast, deep neural networks have proved to be powerful function approximators, successfully modeling complex control policies. In addition, the ability of such networks to learn good representations of high-dimensional sensory inputs makes them a valuable tool when dealing with visual inputs. In this project, we explore the capability of deep neural networks to learn and sequence robot skills for navigation, directly using visual input.
Parallel Entity Resolution with Apache Spark

Entity resolution (ER) is a problem of matching records from different datasets that refer to the same entities. Accurate and fast entity resolution has broad applications in the field of data management. Although many ER approaches have been proposed, it is still a challenge to design a scalable algorithm to cope with massive datasets. In this paper, we propose a parallel entity resolution algorithm with Spark programming model, which can be executed on the computer cluster in parallel. In our approach, we adopt inverted indices and attribute-related TF-IDF weights of tokens to improve the precision and efficiency of the algorithm. Experimental results show that the proposed parallel ER algorithm achieves good accuracy and performance, and the algorithm is scalable for processing entity resolution of large datasets.
Parameterless automatic extrinsic calibration of vehicle mounted lidar-camera systems

This paper presents a new method for automated extrinsic calibration of multi-modal sensors. In particular the paper presents and evaluates a pipeline for calibration of 3D lidar and cameras mounted on a sensor vehicle. Previous methods for multi-modal sensor calibration find the optimal parameters by aligning a set of observations from the different sensor modalities. The main drawback of these methods is the need for a good initialisation in order to avoid converging into a local minima. Our approach eliminates this limitation by combining external observations with motion estimates obtained with the individual sensors. The method operates by utilizing structure from motion based hand-eye calibration to constrain the search space of the optimisation.
An Efficient Energy Conservation Dynamic Consistency Algorithm for SDN Control Plane

Centralized control, the main idea of SDN (Software-Defined Networking) to simplify the control flow, may make SDN suffer from performance bottlenecks. Therefore, we need to use a distributed control plane to overcome the problem. But there are also consistency problem in distributed control plane of SDN. In this paper, we first quantize the consistency, communication costs and availability with relevant research of SDN distributed control plane. Secondly, by normalized calculation, we find the relationship between them. Then we propose a dynamic consistency algorithm for the control plane of SDN. Finally, we corroborate the validity of the algorithm by some experiments. The experimental results show that the proposed algorithm can effectively improve the plane consistency of SDN control.
Some Tricks in Parameter Selection for Extreme Learning Machine

Extreme learning machine (ELM) is a widely used neural network with random weights (NNRW), which has made great contributions to many fields. However, the relationship between the parameters and the performance of ELM has not been fully investigated yet, i.e. the impact of the number of hidden layer nodes, the randomization range of the weights between the input layer and hidden layer, the randomization range of the threshold of hidden nodes, and the type of activation functions. In this paper, eight benchmark functions are used to study this relationship. We have some interesting findings, such as more hidden layer nodes cannot guarantee the best performance of ELM, the empirical randomization range of the hidden weights (i.e., [-1, 1]) and the empirical randomization range of the threshold of hidden nodes (i.e., [0, 1]) may not lead to the optimal performance of ELM models, and ELM with sigmoid as the activation function always achieves better performance on some regression problems than ELM with tribas as the activation function. We hope the findings from our work could provide a useful guidance for researchers to select right parameters for ELM.
Principles of Electromagnetics 5—Wave Applications

Electromagnetics is not an easy subject for students. The subject presents a number of challenges, such as: new math, new physics, new geometry, new insights and difficult problems. As a result, every aspect needs to be presented to students carefully, with thorough mathematics and strong physical insights and even alternative ways of viewing and formulating the subject. The theoretician James Clerk Maxwell and the experimentalist Michael Faraday, both shown on the cover, had high respect for physical insights. This book is written primarily as a text for an undergraduate course in electromagnetics, taken by junior and senior engineering and physics students. The book can also serve as a text for beginning graduate courses by including advanced subjects and problems. The book has been thoroughly class-tested for many years for a two-semester Electromagnetics course at Syracuse University for electrical engineering and physics students. It could also be used for a one-semester course, covering up through Chapter 8 and perhaps skipping Chapter 4 and some other parts. For a one-semester course with more emphasis on waves, the instructor could briefly cover basic materials from statics (mainly Chapters 2 and 6) and then cover Chapters 8 through 12.
Individual, social, and cultural approaches to knowledge sharing

Workplace knowledge sharing is a complex process and there are a large number of studies in the area. In this article three theoretical approaches in library and information science are used to discuss knowledge sharing in the workplace. The approaches are information behavior, social capital, and information culture, and they bring important insights that need to be considered from a holistic management point of view when it comes to knowledge sharing. The individual`s relation to different levels of context is important, meaning both in relation to work roles, work tasks, situations, organizational structures, and culture. The frameworks also shed light on where and how knowledge sharing activities are present in the organization. From a knowledge management point of view, it is important to acknowledge that when knowledge is valued, there is also an awareness of the knowledge sharing activities. Also, in addition to more traditional views of context, the frameworks bring forward different views on context, such as time and space as contextual factors.
The Application of Differential Pulse Cathodic Stripping Voltammetry in the Determination of Trace Copper in Whole Blood

Electrochemical stripping analysis is a very important and widely used tool for the trace determination of various elements and compounds. The method is based on accumulation of Cu (II) ions onto platinum electrode, followed by the reduction of the species by differential pulse cathodic stripping voltammetry. The effect of various parameters such as pH of supporting electrolyte, accumulation of potential and time, scan rate and speed of rotating electrode on the selectivity and sensitivity were studied. The best conditions were found to be pH = 6.5 potassium acetate buffer, the accumulation potential of 0 mV (vs. Ag/AgCl), the accumulation time of 50 s, rotational speed of the working electrode 1500 rpm and the scan rate of 25 mV s-1. The peak current is proportional to the concentration of copper over the range 0.53-107 ng/mL with a detection limit of 0.159 ng/mL and an accumulation time of 50 s. The developed method was validated by the analysis of reference materials certified by total blood.
Understanding Student Perspectives on Self-Efficacy, Scholarship & Library Research in Graduate Social Work Distance Learning: A Pilot Study

ABSTRACT Graduate distance learning programs are charged to find creative ways to foster student engagement and learning. Faculty and Library Sciences professionals are increasingly collaborating to achieve this end. In this study, faculty from the Masters in Social Work program and the Library collaborated to explore student's perceptions about how to best engage the library, its resources and trained staff to improve perceived self-efficacy and scholarship in research methodology. Survey data (n = 28) suggests that these students have a surprisingly high level of discomfort and anxiety related to the information search process, the library, and their own academic self-confidence. Students present as less confident in their ability to locate needed information, more anxious about using the library and asking for help, and appear to have less developed search skills. Implications for student engagement and integration of information literacy skills are presented and discussed.
webMUSHRA — A Comprehensive Framework for Web-based Listening Tests

For a long time, many popular listening test methods, such as ITU-R BS.1534 (MUSHRA), could not be carried out as web-based listening tests, since established web standards did not support all required audio processing features. With the standardization of the Web Audio API, the required features became available and, therefore, also the possibility to implement a wide range of established methods as web-based listening tests. In order to simplify the implementation of MUSHRA listening tests, the development of webMUSHRA was started. By utilizing webMUSHRA, experimenters can configure web-based MUSHRA listening tests without the need of web programming expertise. Today, webMUSHRA supports many more listening test methods, such as ITU-R BS.1116 and forced-choice procedures. Moreover, webMUSHRA is highly customizable and has been used in many auditory studies for different purposes.
A Partial String Matching Approach for Named Entity Recognition in Unstructured Bengali Data

— In today's data driven, automated and digitized world, a significant stage of information extraction is to look for special keywords, more formally known as 'Named Entity'. This has been an active research topic for more than two decades and significant progresses have been made. Today we have models powered by deep learning that, although not perfect, have near human level accuracy on certain occasions. Unfortunately these algorithms require a lot of annotated training data, which we hardly have for Bengali language. This paper proposes a partial string matching approach to identify a named entity from an unstructured text corpus in Bengali. The algorithm is a partial string matching technique, based on Breadth First Search (BFS) search on a Trie data structure, augmented with dynamic programming. This technique is capable of not only identifying named-entities present on a text, but also estimating the actual named-entities from erroneous data. To evaluate the proposed technique, we conducted experiments in a closed domain where we employed this approach on a text corpus with some predefined named entities. The texts experimented on was both structured and unstructured, and our algorithm managed to succeed in both the cases.
Using Unsupervised Machine Learning for Outlier Detection in Data to Improve Wind Power Production Prediction

The expansion of wind power for electrical energy production has increased in recent years and shows no signs of slowing down. This unpredictable source of energy has contributed to destabilization of the electrical grid causing the the energy market prices to vary significantly on a daily basis. For energy producers and consumers to make good investments, methods have been developed to make predictions of wind power production. These methods are often based on machine learning were historical weather prognosis and wind power production data is used. However, the data often contain outliers, causing the machine learning methods to create inaccurate predictions. The goal of this Master’s Thesis was to identify and remove these outliers from the data so that the accuracy of machine learning predictions can improve. To do this an outlier detection method using unsupervised clustering has been developed and research has been made on the subject of using machine learning for outlier detection and wind power production prediction.