Miracle-dz/UniFAR-FTUs huggingface dataset:
Introducing PBL in Transportation Engineering

This paper describes an attempt to introduce a problem-based learning project into the Transportation Engineering course at the National University of Singapore. For the project, students were divided into groups of ten to evaluate the signal timings of a real-life intersection and to make changes to improve its operation. The Land Transport Authority provided flow data and existing signal timings but the students obtained all other data needed for the project by themselves. With the lecturer serving only as a facilitator, the students were left to organise themselves and acquire relevant knowledge to deal with the ill-defined problem. In the process, students had to contend with less-than ideal conditions, measurement of non-homogenous traffic, missing data and non-unique approaches to the problem. The students were evaluated based on their individual participation in the discussion and their group presentation of findings and experience. From the feedback survey carried out two months after the project, the students indicated strong learning benefits from the experience. Students of the leader-type and organiser-type appreciated their learning in critical thinking and decision-making skills while those who took on the role of resource providers gained in knowledge. Despite the positive interest shown in PBL; students still prefer tutorials and lectures because they tend to acquire knowledge and skills faster through the conventional methods. However, based on an assessment on the confidence level in solving problems, there is evidence to suggest that students are better able to retain knowledge and skills learned through a PBL project.
A non-divergent estimation algorithm in the presence of unknown correlations

This paper addresses the problem of estimation when the cross-correlation in the errors between different random variables are unknown. A new data fusion algorithm, the covariance intersection algorithm (CI), is presented. It is proved that this algorithm yields consistent estimates irrespective of the actual correlations. This property is illustrated in an application of decentralised estimation where it is impossible to consistently use a Kalman filter.
A Prototype Machine Translation System

Machine translation, especially machine translation for sign languages, remains an active research area. Sign language machine translation presents unique challenges to the whole machine translation process. In this thesis a prototype machine translation system is presented. This system is designed to translate English text into a gloss based representation of South African Sign Language (SASL). In order to perform the machine translation, a transfer based approach was taken. English text is parsed into an intermediate representation. Translation rules are then applied to this intermediate representation to transform it into an equivalent intermediate representation for the SASL glosses. For both these intermediate representations, a tree adjoining grammar (TAG) formalism is used. As part of the prototype machine translation system, a TAG parser was implemented. The translation rules used by the system were derived from a SASL phrase book. This phrase book was also used to create a small SASL gloss TAG. Lastly, some additional tools, for the editing of TAG trees, were also added to the prototype system.
System‐Level Performance of Limited Feedback Schemes for Massive MIMO

To implement high‐order multiuser multiple input and multiple output (MU‐MIMO) for massive MIMO systems, there must be a feedback scheme that can warrant its performance with a limited signaling overhead. The interference‐to‐noise ratio can be a basis for a novel form of Codebook (CB)‐based MU‐MIMO feedback scheme. The objective of this paper is to verify such a scheme's performance under a practical system configuration with a 3D channel model in various radio environments. We evaluate the performance of various CB‐based feedback schemes with different types of overhead reduction approaches, providing an experimental ground with which to optimize a CB‐based MU‐MIMO feedback scheme while identifying the design constraints for a massive MIMO system.
High-speed sensory-motor fusion based on dynamics matching

This paper discusses a design concept of a sensory-motor fusion system to achieve high performance in a dynamic changing environment. From the viewpoint of a dynamic system, the new concept called "dynamics matching" is proposed to match the dynamics constraints of a system. Based on this concept, we describe a high-speed vision chip that has a general purpose parallel processing array along with a photodetector all in a single silicon chip. Next we describe a new sensory-motor fusion system which consists of a hierarchical parallel processing system, a vision chip system, and a multifingered hand-arm. All sensory feedback, including visual feedback, can be achieved in 1 ms. In addition, as an application of the system, we demonstrate high-speed grasping using visual and force feedback.
Network-on-Chips on 3-D ICs: Past, Present, and Future

Abstract Interconnects have become the chief bottleneck in today’s era of chip design. Along the road of interconnect evolution, Network-on-Chips (NoCs) have emerged as a structured and scalable solution for connecting computational elements on a very large scale integration chip. Also, with the deep-submicron technology allowing integration of billions of transistors, chips have grown very complex and large in size. The global wire-length problem was addressed with the integration of devices in the third dimension (3-D). The combination of 3-D integration and a scalable interconnect, like NoCs, promise to revolutionize design for Chip Multi-processors, System-on-chips, and System-in-package. This paper surveys on all the advancements in 3-D NoCs.
Fracture detection with artificial intelligence: improved accuracy with region of interest focusing

Poster: "ECR 2018 / C-1266 / Fracture detection with artificial intelligence: improved accuracy with region of interest focusing" by: "M. D. V. Thurston1, T. Mackinnon1, D. H. Kim2; 1Plymouth/UK, 2Exeter/UK"
Impact of Computer Remote Education on CLL Learning and Solutions

This paper aims at some impacts of computer remote education on the Chinese Language and Literature (CLL) learning of Chinese overseas students with a learning software enabled with remote education as a study case, to conduct a survey and analysis on how this type of learning software serves the students when they study and what is the best commercial learning software in the market by questionnaire survey, statistical analysis and summary methods. On this basis, it is concluded that the CLL learning software plays an important role in improving the learning efficiency and level of Chinese overseas students. But beyond that, the existing learning software in the market still have many limitations. In the end, to make such learning software play a better role in overseas students’ learning process, this paper proposes strategies for upgrading software in question by virtue of operating and popularizing more mature English learning software with remote education function, which provides a valuable reference for future improvement and promotion of similar software
Analyzing Internet Connectivity Data Using Modified k-shell Analysis

Simulating the Internet is a complex problem. To be able to do that extensive analysis for the Internet structure needs to be carried out. This is done to maintain the main characteristics of the growth of the Internet and gain further understanding about its structure. We use the k-shell decomposition algorithm to analyze and visualize the Internet on the Autonomous System level (AS-level). The data used for this analysis is gathered from two major AS-connectivity measurement efforts (i.e CAIDA and DIMES). As well as analyzing the data gathered, a validation process is applied on this data to ensure the reliability of the Autonomous Systems (ASes) edges, then the validated data is analyzed using k-shell decomposition. Furthermore, we extend k-shell decomposition to account for the directedness of the AS graph. Afterwards, we compare the results gained before and after the validation process to explore the impact of removing the invalid ASes edges on the Internet’s structure and its connectivity.
CF-Pursuit: A Pursuit Method with a Clothoid Fitting and a Fuzzy Controller for Autonomous Vehicles

Simple and efficient geometric controllers, like Pure-Pursuit, have been widely used in various types of autonomous vehicles to solve tracking problems. In this paper, we have developed a new pursuit method, named CF-Pursuit, which has been based on Pure-Pursuit but with certain differences. In CF-Pursuit, in order to reduce fitting errors, we used a clothoid C1 curve to replace the circle employed in Pure-Pursuit. This improvement to the fitting method helps the Pursuit method to decrease tracking errors. As regards the selection of look-ahead distance, we employed a fuzzy system to directly consider the path's curvature. There are three input variables in this fuzzy system, 6mcurvature, 9mcurvature and 12mcurvature, calculated from the clothoid fit with the current position and the goal position on the defined path. A Sugeno fuzzy model was adapted to output a reasonable look-ahead distance using the experiences of human drivers as well as our own tests. Compared with some other geometric controllers, CF-Pursuit performs better in robustness, cross track errors and stability. The results from field tests have proven the CF-Pursuit is a practical and efficient geometric method for the path tracking problems of autonomous vehicles.
Exploration in a gridworld which has a cliff

In this project, we study machine learning techniques such as Genetic Algorithm (GA), artificial Neural Network (NN), Reinforcement Learning (RL), and so on, by observing an artificial agent who travels a virtual world called a gridworld. The world has the point for the agent to start with, the point for the agent to aim as a goal, and dangerous cliff to which if the agent fall the agent will die.
Adaptive Cartesian meshes for atmospheric single-column models: a study using Basilisk 18-02-16

Abstract. It is well known that the representation of certain atmospheric conditions in climate and weather models can still suffer from the limited grid resolution that is facilitated by modern-day computer systems. Herein we study a simple one-dimensional analogy to those models by using a single-column model description of the atmosphere. The model employs an adaptive Cartesian mesh that applies a high-resolution mesh only when and where it is required. The so-called adaptive-grid model is described, and we report our findings obtained for tests to evaluate the representation of the atmospheric boundary layer, based on the first two GEWEX ABL Study (GABLS) inter-comparison cases. The analysis shows that the adaptive-grid algorithm is indeed able to dynamically coarsen and refine the numerical grid whilst maintaining an accurate solution. This is an interesting result as in reality, transitional dynamics (e.g. due to the diurnal cycle or due to changing synoptic conditions) are the rule rather than the exception.
Chapter 6. Graphics and invention in academic engineers’ writing for publication

This chapter explores how academic engineers write for publication, focusing on “invention” – that is, moments when writers identify the research results they want to present and decide on the arguments they want to make in an article. A key finding presented is that beyond the well-documented role of graphics in displaying research results, graphics also play a crucial heuristic role in invention. This finding emerged from an ethnographic study of three engineering research groups, which entailed the analysis of a range of qualitative data to offer perspectives on the experiences of academic engineers writing for publication. Drawing on this research, the chapter documents that in developing texts for publication, engineers often begin with the graphic results of data analysis to identify findings and begin to craft arguments. Further, in research group and informal meetings, engineers invoke the notion of storytelling through graphics as they socialize their group members into the practices of research dissemination via posters and articles.
Reduced Order Observer Based Load Frequency Control Using Pole-Placement Design

In a power system, as a power load demand varies randomly, both area frequency and line power also vary. The objectives of load frequency control (LFC) are to minimize the transient deviations in these variables (area frequency and line power) and to ensure their steady state errors to be zeros. When dealing with the LFC problem of power systems, unexpected external disturbances, parameter uncertainties and the model uncertainties of the power system pose big challenges for controller design. In this project, a reduced-order state observer with a practical point of view for LFC problem in a power system is proposed based on the pole-placement method. A proper choice of closed-loop pole location has been proposed using symmetrical root locus (SRL) method. In the practical environment, there is limited access to all state variables of system and measuring all of them is usually impossible. So when the available sensors are less than the number of states or when it may be undesirable, expensive or impossible to measure directly all of the states, a reduced-order state observer can be applied as proposed in this project. The proposed strategy is tested on a single area power system. The dynamic model of the power system and the controller design based on the model are elaborated. The robustness and stability of the control schemes is examined through simulations.
Analysis and Cancellation Methods of Second Order Intermodulation Distortion in RFIC Downconversion Mixers Analyse und Methoden zur Unterdruckung von Storungen durch Intermodulation zweiter Ordnung in RFIC-Abwartsmischern

This dissertation presents analysis and explores cancellation methods of second order intermodulation distortion (IMD2) in radio frequency integrated circuit (RFIC) downconversion mixers. Such methods enable improvement of the overall receiver dynamic range by enhancing its immunity to amplitude modulated interferers. High rejection of IMD2 is especially important in case of wireless transceivers with decreased RF selectivity, where strong out-of-band signals may be present at the downconversion mixer input. In the thesis, a thorough study of second order intermodulation generation mechanisms is carried out, with a stress put on sensitivity of second-order intercept point (IP2) to various transceiver operating conditions. Different classes of IMD2 cancellation techniques are investigated. A concept of IP2 auto-calibration based on adaptive digital signal processing is presented and measurement results of the corresponding hardware demonstrator implemented in an advanced 0. 13μm RF CMOS technology are shown.
Text Document Clustering based on Semantics

document clustering plays an important role in providing intuitive navigation and browsing mechanisms by organizing large sets of documents into a small number of meaningful clusters. Clustering is a very powerful data mining technique for topic discovery from text documents. The partitional clustering algorithms, such as the family of K-means, are reported performing well on document clustering. They treat the clustering problem as an optimization process of grouping documents into k clusters so that a particular criterion function is minimized or maximized. The bag of words representation used for these clustering is often unsatisfactory as it ignores relationships between important terms that do not co-occur literally. In order to deal with the problem, we integrate core ontologies as background knowledge into the process of clustering text documents. This model combines phrases analysis as well as words analysis with the use of WordNet as background Knowledge and NLP to explore better ways of document representation for clustering. The Semantic based analysis assigns semantic weights to both document words and phrases. The new weights reflect the semantic relatedness between the documents terms and capture the semantic information in the documents to improve the web document clustering. The method adopted has been evaluated on different data sets with standard performance measures to develop meaningful clusters has been proved.
Action research on the application of technology assisted urban gaming in language education in a Polish upper-secondary school

ABSTRACT Being able to communicate in foreign languages, along with the remaining key competences for lifelong learning, are of special significance for upper-secondary school students – soon-to-be tertiary education students and employees. Although Polish educational authorities have made efforts to account for this need in the Core Curriculum for Modern Languages, the provisions are pursued with difficulty in school practice. The aim of this paper is to address this challenge by exploring and evaluating the feasibility and student-participant perceptions of urban gaming in school settings as a didactic tool that facilitates language education, as well as digital, social, and learning to learn skills development in an upper-secondary school. Accordingly, the paper presents the design, implementation and evaluation of a technology assisted urban game named ‘As it once was in Cracow—discovering the history of the city’, developed for a group of Polish upper-secondary school students (N = 25) from Warsaw. The results provide evidence for student-perceived educational gains with regard to the development of skills such as teamwork, digital literacy, the English language, and content (history) knowledge.
Enhancement of esophageal speech using voice conversion techniques

This paper presents a novel approach for enhancing esophageal speech using voice conversion techniques. Esophageal speech (ES) is an alternative voice that allows a patient with no vocal cords to produce sounds after total laryngectomy: this voice has a poor degree of intelligibility and a poor quality. To address this issue, we propose a speaking-aid system enhancing ES in order to clarify and make it more natural. Given the specificity of ES, in this study we propose to apply a new voice conversion technique taking into account the particularity of the pathological vocal apparatus. We trained deep neural networks (DNNs) and Gaussian mixture models (GMMs) to predict " laryngeal " vocal tract features from esophageal speech. The converted vectors are then used to estimate the excitation cepstral coefficients and phase by a search in the target training space previously encoded as a binary tree. The voice resynthesized sounds like a laryngeal voice i.e., is more natural than the original ES, with an effective reconstruction of the prosodic information while retaining , and this is the highlight of our study, the characteristics of the vocal tract inherent to the source speaker. The results of voice conversion evaluated using objective and subjective experiments , validate the proposed approach.
The imperative for proactive cooperative national mapping strategies in the new millennium

There is no comprehensive global mapping program in existence today, despite the efforts of those involved in the International Map of the World project begun in the late 1800s. While the IMW project was finally declared ended in the 1980s, there are now several current projects that will help fill some of the gaps in the mapping needs of a variety of users, including the Global Map project. In addition to a brief review of this project, an example of the application of Global Map data to local phenomena is given.
Exploring School Foodservice Directors’ Intentions to Implement Farm-To-School Procurement Methods Considering Food Safety Practices

This study explored the intentions of foodservice directors' (FSD) who oversee Farm-to-School (F2S) programs to implement alternative procurement methods that better account for food safety practices. A web-based questionnaire was distributed to 864 California school FSDs with 136 (15.7%) usable surveys returned. Findings revealed that although FSDs understand the importance of food safety training and have confidence in their ability to manage produce safety practices, they express little intention to change their procurement practices. This finding might indicate a need for more resources and supportive policies.
Measuring the parameters of a cage-rotor reluctance synchronous motor

A three-phase two-pole line-start conventional rotor reluctance synchronous machine that comprises a cage was tested in the laboratory for the determination of its dynamic characteristics. The purpose is to obtain the machine equivalent circuit for synchronous and asynchronous operation modes. The vector diagram is the theoretical approach that is used in this work to get the synchronous performance modeling of the machine. Among other parameters with straightforward measurement procedures, the d-axis and q-axis machine reactance are required in the model. It is well known that the measurement of some parameters of cage-rotor machines may present some difficulty owing to the distortion that is introduced by the cage. The q-axis is one of these critical parameters to be measured. The question is how to obtain accurate parameter values from measurements if a damper or a cage is present. In this work the d-axis reactance was obtained via the conventional no-load test. As for the determination of the q-axis reactance, the steady-state no-load and load tests devised by V.B. Honsinger (1971) were applied. The variation of some parameters with the frequency was obtained by feeding the machine with an inverter. Different stator voltages were applied as well. The torque-speed curves of the machine were also obtained. The setup details a list of the measured parameters and other results are presented in the paper.
GPU Remote Memory Access Programming

High performance computing studies the construction and programming of computing system with tremendous computational power playing a key role in scientific computing and research across disciplines. The graphics processing unit (GPU) developed for fast 2D and 3D visualizations has turned into a programmable general purpose accelerator device boosting today’s high performance clusters. Leveraging these computational resources requires good programming model abstractions to manage system complexity. Today’s state of the art employs separate cluster communication and GPU computation models such as MPI and CUDA. The bulk-synchronous nature of CUDA and the role of the GPU as a CPU-dependent co-processor limits cluster utilization. In this master thesis we devise, implement, and evaluate the novel GPU cluster programming model GPU RMA addressing three key areas. GPU RMA introduces a simplifying abstraction exposing the cluster as a set of interacting ranks. The ranks execute on the GPU removing the complexity of explicit GPU management from the host. GPU RMA introduces communication amongst ranks on local and remote devices allowing more fine-grained communication and synchronization compared to conventional models increasing concurrency and resource usage. GPU RMA implements one-sided notified access communication leveraging the recently introduced RDMA support for GPUs providing richer communication semantics than current solutions. We implemented GPU RMA on a cluster with Intel CPUs, Nvidia GPUs, and an InfiniBand interconnect, studied the impact of system components to communication latency, and derived an empirical performance model. We performed a case study to assess GPU RMA performance compared to the established MPI and CUDA approach and validated our performance model. Our experiments show GPU RMA improves performance up to 25% compared to the state of the art and encourages further study of GPU RMA performance and scalability.