Miracle-dz/UniFAR-FTUs huggingface dataset:
THD minimization for Z-source-based inverters with a novel sinusoidal PWMswitching method

: This paper presents a modified sinusoidal pulse width modulation (SPWM) switching method for one-phase and investigated two-phase impedance-source inverter structures. The proposed structure generates pulses for a quasi-Z-source converter and this block produces a unilateral voltage sine wave in the block’s output. This signal is applied to the inverter as its input wave. For this purpose, the novel SPWM method is proposed for power switches while being switched complementarily. Two power switches are used in the structure to generate the pure sinusoidal output voltage and to minimize total harmonic distortion (THD), which is an essential parameter in inverter design. The results show that the proposed method generates the pure sinusoidal voltage and current signals for resistive and inductive loads and pure voltage and improved current waves for capacitive loads in comparison with existing techniques, since the THD of the output voltage and current signals is strongly affected by the dynamic loads. This method leads to final cost improvement and reduction of the size of the system with fewer number of components, which are essential parameters for renewable energy resource applications. A mathematical model is validated with the 2017a version of MATLAB/Simulink and 1.51% and 1.33% THD values are reported for low and high power loads, respectively, in the one-phase structure and 0.95% and 0.87% in the two-phase system. Finally, a 120 W prototype has been implemented and tested. A sine-wave with 620 Vac peak to peak amplitude and 50 Hz frequency has been gained in the inverter’s output and the quality of the voltage and current waveforms has been evaluated for different two 1.8 k Ω and 600 Ω resistive loads in the one-phase structure. Experimental results confirm all mathematical and simulation results.
Integration of a road network into a radar ground moving target tracking (GMTT) system and its performance evaluation

This paper discusses the application of multiple hypothesis tracking (MHT) to the processing of ground target data collected with a long range surveillance radar. A key element in the successful tracking of ground targets is the use of road networks. Thus, the paper begins with an overview of the alternative approaches that have been considered for incorporating road data into a ground target tracker and then it gives a detailed description of the methods that have been chosen. The major design issues to be addressed include the manner in which road filter models are included into a Variable-Structure Interacting Multiple Model (IMM) filtering scheme, how the road filter models are chosen to handle winding roads and intersections, and the tracking of targets that go on and off-road. Performance will be illustrated using simulated data and real data collected from a large surveillance area with a GMTI radar. The area considered contains regions of heavy to moderate target densities and clutter. Since the real data included only targets of opportunity (TOO), it was necessary to define metrics to evaluate relative performance as alternative tracking methods/parameters are considered. These metrics are discussed and comparative results are presented.
A Quaternary-Encoding-Based Channel Hopping Algorithm for Blind Rendezvous in Distributed IoTs

In distributed Internet of Things (IoTs), channel hopping (CH) is an effective scheme for neighbor nodes to achieve blind rendezvous over common available channels and to establish communication links. When nodes are unaware of each other’s local clocks and the global channels and have no pre-assigned CH strategies or identifiers (IDs), it is particularly challenging to guarantee blind rendezvous within a finite period of time, which has not been solved yet by using only one radio. In this paper, we propose a novel quaternary-encoding-based CH (QECH) algorithm to tackle the above issue. The QECH algorithm encodes a randomly selected channel into a quaternary string according to the 6B/8B encoding. We also append a common prefix string as well as the randomly selected channel before the quaternary string to guarantee overlaps in the asynchronous scenario. For all kinds of quaternary digits, we construct four mutually co-prime numbers to enumerate all possible combinations of the common available channels. We theoretically analyze the deterministic rendezvous principle and the upper bounded rendezvous latency of the QECH algorithm. We also verify the effectiveness of the QECH algorithm through extensive simulations. Evaluation results show the superiority of the QECH algorithm in terms of rendezvous latency.
A Data Augmentation-Based Framework to Handle Class Imbalance Problem for Alzheimer’s Stage Detection

Alzheimer’s Disease (AD) is the most common form of dementia. It gradually increases from mild stage to severe, affecting the ability to perform common daily tasks without assistance. It is a neurodegenerative illness, presently having no specified cure. Computer-Aided Diagnostic Systems have played an important role to help physicians to identify AD. However, the diagnosis of AD into its four stages; No Dementia, Very Mild Dementia, Mild Dementia, and Moderate Dementia remains an open research area. Deep learning assisted computer-aided solutions are proved to be more useful because of their high accuracy. However, the most common problem with deep learning architecture is that large training data is required. Furthermore, the samples should be evenly distributed among the classes to avoid the class imbalance problem. The publicly available dataset (OASIS) has serious class imbalance problem. In this research, we employed a transfer learning-based technique using data augmentation for 3D Magnetic Resonance Imaging (MRI) views from OASIS dataset. The accuracy of the proposed model utilizing a single view of the brain MRI is 98.41% while using 3D-views is 95.11%. The proposed system outperformed the existing techniques for Alzheimer disease stages.
Understanding users’ participation in online health communities: A social capital perspective

Due to the intense competition, it is crucial for online health communities to facilitate users’ participation and retain them. Drawing on the social capital theory, this research examined users’ participation in online health communities. The results indicated that social support, which includes informational support and emotional support, has a significant effect on social capital, which in turn affects users’ participation including health knowledge acquisition and contribution. The results imply that online health communities need to create a supportive climate in order to develop social capital and facilitate users’ participation.
The Social Media Party: Fear of Missing Out (FoMO), Social Media Intensity, Connection, and Well-Being

ABSTRACT The human need to belong is an innate drive that dictates much of our behavior. Informed by The Belongingness Hypothesis and Information Foraging Theory, the present study examines the relationship between FoMO and well-being. Study 1 (107 college students) investigates the relationship between FoMO, social media intensity and social connection. Results find that FoMO is positively associated with social media intensity, but negatively associated with social connection. The mediation tests, interestingly, reveal more positive results regarding FoMO. Specifically, FoMO has a positive indirect effect on social connection through social media intensity, suggesting that FoMO may, in some cases, be a good thing leading to enhanced social connection. Study 2 (458 college students) finds that FoMO impacts subjective well-being both directly (negatively) and indirectly (positively) through its impact on social media intensity and social connection. Results of the two studies reveal a nuanced model of FoMO and its relationships with social media intensity, connection, and well-being. FoMO can have a positive impact on well-being if it leads to social media use that fosters social connection. Study limitations and future research directions are discussed.
WorldIndex database update 2018

The primary software program for designing and evaluating pavements on military installations is the Pavement-Transportation Computer Assisted Structural Engineering (PCASE) program. PCASE, which is undergoing an update to version PCASE7, is an engineering tool for the design and evaluation of airfields and roadways using Department of Defense criteria. The WorldIndex database is a necessary component of PCASE and is integrated into it. The WorldIndex database internally supplies PCASE with the climate parameters required to determine the maximum depth of frost penetration. The database contains other temperature-based parameters used in structural evaluations. This report documents a recent update to the WorldIndex database that uses historical surface air-temperature observations from 1980 to 2017 at over 10,000 l0cations around the globe. The database contains 80 airtemperature-based parameters determined for each station, including the three parameters that PCASE requires to compute the maximum depth of frost penetration for pavement design in cold regions: the average annual air temperature, the average freezing degree-days, and the mean length of the freezing season. The report concludes with recommendations for future updates. DISCLAIMER: The contents of this report are not to be used for advertising, publication, or promotional purposes. Citation of trade names does not constitute an official endorsement or approval of the use of such commercial products. All product names and trademarks cited are the property of their respective owners. The findings of this report are not to be construed as an official Department of the Army position unless so designated by other authorized documents. DESTROY THIS REPORT WHEN NO LONGER NEEDED. DO NOT RETURN IT TO THE ORIGINATOR. ERDC/CRREL TR-19-13 iii
Investigation into Stand-alone Brain-computer Interfaces for Musical Applications

Brain-computer interfaces (BCIs) aim to establish a communication medium that is independent of muscle control. This project investigates how BCIs can be harnessed for musical applications. The impact of such systems is twofold — (i) it offers a novel mechanism of control for musicians during performance and (ii) it is beneficial for patients who are suffering from motor disabilities. Several challenges are encountered when attempting to move these technologies from laboratories to real-world scenarios. Additionally, BCIs are significantly different from conventional computer interfaces and realise low communication rates. This project considers these challenges and uses a dry and wireless electroencephalogram (EEG) headset to detect neural activity. It adopts a paradigm called steady state visually evoked potential (SSVEP) to provide the user with control. It aims to encapsulate all braincomputer music interface (BCMI)-based operations into a stand-alone application, which would improve the portability of BCMIs. This projects addresses various engineering problems that are faced while developing a stand-alone BCMI. In order to efficiently present the visual stimulus for SSVEP, it requires hardware-accelerated rendering. EEG data is received from the headset through Bluetooth and thus, a dedicated thread is designed to receive signals. As this thesis is not using medical-grade equipment to detect EEG, signal processing techniques need to be examined to improve the signal to noise ratio (SNR) of brain waves. This projects adopts canonical correlation analysis (CCA), which is multi-variate statistical technique and explores filtering algorithms to improve communication rates of BCMIs. Furthermore, this project delves into optimising biomedical engineering-based parameters, such as placement of the EEG headset and size of the visual stimulus. After implementing the optimisations, for a time window of 4s and 2s, the mean accuracies of the BCMI are 97.92±2.22% and 88.02±9.30% respectively. The obtained information transfer rate (ITR) is 36.56± 9.17 bits min-1, which surpasses communication rates of earlier BCMIs. This thesis concludes by building a system which encompasses a novel control flow, which allows the user to play a musical instrument by gazing at it.
Importance-based signal detection and parameter estimation with applications tonew particle search

One of the hardest challenges in data analysis is perhaps the detection of rare anomalous data buried in a huge normal background. We study this problem by constructing a novel method, which is a combination of the Kullback–Leibler importance estimation procedure based anomaly detection algorithm and linear discriminant classifier. We choose to illustrate it with the example of charged Higgs boson (CHB) search in particle physics. Indeed, the Large Hadron Collider experiments at CERN ensure that CHB signal must be a tiny effect within the irreducible W-boson background. In simulations, different CHB events with different characteristics are produced and judiciously mixed with the non-CHB data, and the proposed method is applied. Our results show that distribution parameters of weak CHB signals can be estimated with high performance. This anomaly detection method is general enough to apply to similar problems in other fields (e.g., astrophysical, medical, engineering problems).
Towards enabling human-robot handovers : exploring nonverbal cues for fluent human-robot handovers

Fundamental human to human interactions sharing spaces, tools, handing over objects, carrying objects together are part of the everyday experience; for most people, the task of handing over an object to another person is a natural and seemingly effortless task. However, in the context of human-robot interaction, smooth and seamless interaction is an open problem of fundamental interest for robotics designers, integrators and users alike. This thesis explores how nonverbal cues exhibited during robot giving and receiving behaviours change how users perceive the robot, and affect the handover task. Additionally, the work also investigates how robots can recognize and interpret expressions conveyed by a human giver to infer handover intent. Over the course of several user studies examining human-human and humanrobot handovers, the role of nonverbal cues such as gaze and object orientation and how they may play a part in establishing fluency and efficiency of robot-tohuman handovers are investigated. These studies provide insights into how robots can be trained through observation of human-to-human handovers. Furthermore, this thesis examines the role of nonverbal cues in the less-studied human-to-robot handover interaction. In this exploration, kinematic features from motion-captured skeleton models of a giver are used to establish the intent to handover, thereby enabling a robot to appropriately react and receive the object. Additionally, changing user perceptions, geometry and dynamics of human-to-robot handovers are explored through variation of initial pose, grasp type during, and retraction speed after handover of the robot receiver. Findings from this thesis demonstrate that nonverbal cues such as gaze and object orientation in the case of robot-to-human handovers, and kinodynamics during
DeliveryCoin: An IDS and Blockchain-Based Delivery Framework for Drone-Delivered Services

In this paper, we propose an intrusion detection system (IDS) and Blockchain-based delivery framework, called DeliveryCoin, for drone-delivered services. The DeliveryCoin framework consists of four phases, including system initialization phase, creating the block, updating the blockchain, and intrusion detection phase. To achieve privacy-preservation, the DeliveryCoin framework employs hash functions and short signatures without random oracles and the Strong Diffie–Hellman (SDH) assumption in bilinear groups. To achieve consensus inside the blockchain-based delivery platform, we introduce a UAV-aided forwarding mechanism, named pBFTF. We also propose an IDS system in each macro eNB (5G) for detecting self-driving network attacks as well as false transactions between self-driving nodes. Furthermore, extensive simulations are conducted, and results confirm the efficiency of our proposed DeliveryCoin framework in terms of latency of blockchain consensus and accuracy.
Validation of the 'Haptic Cow': a simulator for training veterinary students

A virtual reality simulator, the Haptic Cow, has been developed using touch feedback technology for training veterinary students to perform bovine rectal palpation of the reproductive tract. The simulator was designed to supplement existing training and address some of the difficulties associated with teaching palpation-based skills. Students need to achieve a certain level of proficiency by graduation but this has become increasingly difficult because of problems with current training methods and a reduction in the number of opportunities to practice. A simulator- based teaching tool was developed as a potential solution. The first step involved designing a simulator on the basis of requirements established through consultation with both veterinary surgeons, as teachers, and students, as learners. Research was then undertaken to validate the simulator by following a set of established criteria described for the evaluation of new technologies used in medical education. The virtual models were assessed by experts as realistic enough representations of the same structures in the cow. An experiment to assess the effect of simulator training compared the performance of one group of students, whose training was supplemented with a simulator session, with another group of traditionally trained students. The subsequent performance for finding and identifying the uterus when examining cows for the first time, was significantly better for the simulator trained group, indicating that skills learned in the simulator environment transferred to the real task. A project was also undertaken to integrate the simulator into a curriculum, with training included as part of the farm animal course at the University of Glasgow Veterinary School. The training was well received by students, useful feedback was gathered and the simulator continues to be used as part of the course. Further developments were undertaken with the aim of creating a more versatile teaching tool and addressing some of the questions and issues raised. An automated version of the Haptic Cow was designed for students to use on their own, with computer guidance replacing the instructor's role. An evaluation found that the new version of the teaching tool was both usable and an effective way of equipping students with the skills required to find and identify the uterus. The potential to use haptic technology to investigate various aspects of performance was also explored in relation to the question of hand choice for certain palpation-based skills: differentiating between objects on the basis of softness and size. Ongoing research and development options are discussed, with the aim of building on the current work by expanding the role of haptic technology in veterinary education in the future.
Routing problems in order picking

In manual picker-to-part order picking systems, human operators (order pickers) walk or ride through the warehouse using a picking device in order to retrieve items which are specified by customer orders. The major part of the working time, an order picker spends for traveling through the warehouse. Therefore, finding short picker tours is pivotal for an efficient organization of warehouse operations. The construction of picker tours is part of the Picker Routing Problem (PRP). The PRP is characterized by the arrangement of the storage locations in the picking area (layout). In the first part of this paper, attributes regarding the layout are pointed out which affect the types of decisions to be made and the time complexity of the solution approaches to the respective PRPs. In the second part, the integration of the PRP into the Order Batching Problem (OBP), which deals with the grouping of customer orders into picking orders, is considered. Since the PRP and the OBP always arise simultaneously, an integrated solution of both problems has received much attention in the recent literature. However, solution approaches are rarely based on the same settings. Therefore, the algorithms are classified according to the underlying assumptions here in order to obtain a structured overview. Finally, for both PRPs and integrated problems, research gaps are identified.
Generation of music pieces using machine learning: long short-term memory neural networks approach

Abstract In this article, we explore the usage of long short-term memory neural network (NN) in generating music pieces and propose an approach to do so. Bach’s musical style has been selected to train the NN to make it able to generate similar music pieces. The proposed approach takes midi files, converting them to song files and then encoding them to be as inputs for the NN. Before inputting the files into the NNs, an augmentation process which augments the file into different keys is performed then the file is fed into the NN for training. The last step is the music generation. The main objective is to provide the NN with an arbitrary note and then the NN starts amending it gradually until producing a good piece of music. Various experiments have been conducted to explore the best values of parameters that can be selected to obtain good music generations. The obtained generated music pieces are accepted in terms of rhythm and harmony; however, some other problems exist such as in certain cases the tone stops or in some other cases getting short melodies that do not change.
Using Information Technology: A Practical Introduction to Computers & Communications

From the Publisher: Using Information Technology,4/e integrates solid,practical coverage of communications and the Internet/WWW throughout while covering the fundamental computing concepts surrounding the digital age including software,hardware,data,people,and procedures. The 4/e incorporates the strong running themes listed below,all centered around educating today's consumer and demonstrating how the changing world of technology has such a major influence on our lives and the decisions we make with regard to the challenges faced as well as the endless promises that lie ahead. 5integrated running themes key to creating a savvy,internet consumer include: 1) How to Buy/Maintain a PC 2) The Nuts and Bolts of a PC 3) How to Select an Internet Service Provider (ISP) and Getting Connected 4) How to Be a Smart Internet Consumer 5) Ethical Issues Related to Protecting Your Identity from Getting Stolen
Reality-based interaction affecting mental workload in virtual reality mental arithmetic training

ABSTRACT The concept of digital game-based learning (DGBL) evolves rapidly together with technological enhancements of virtual reality (VR) and smart phones. However, the mental workload (MWL) that VR-training applications demand and motivational qualities originating from user experience (UX) should be identified in order to create effective and enjoyable training/learning challenges that fit with individual users’ capabilities. This study examined the effects of reality-based interaction (RBI) and VR on measures of student motivation and MWL, in a mental arithmetic game for secondary school pupils. In a randomised controlled trial with sixty school children, a mental arithmetic game was tested with three different interaction and two different presentation methods – VR RBI, VR head-mounted-display tapping and tablet flick-gesture. Results found a significant effect of RBI on MWL but no differences in enjoyment of training were found between VR-experience and tablet training-experience. In fact, adding the gaming-context to the mental arithmetic task created an enjoyable, motivating experience regardless of presentation or interaction-style.