Miracle-dz/UniFAR-FTUs huggingface dataset:
Navigation Comparison between a Real and a Virtual Museum: Time-dependent Differences using a Head Mounted Display

The validity of environmental simulations depends on their capacity to replicate responses produced in physical environments. However, very few studies validate navigation differences in immersive virtual environments, even though these can radically condition space perception and therefore alter the various evoked responses. The objective of this paper is to validate environmental simulations using 3D environments and head-mounted display devices, at behavioural level through navigation. A comparison is undertaken between the free exploration of an art exhibition in a physical museum and a simulation of the same experience. As a first perception validation, the virtual museum shows a high degree of presence. Movement patterns in both ‘museums’ show close similarities, and present significant differences at the beginning of the exploration in terms of the percentage of area explored and the time taken to undertake the tours. Therefore, the results show there are significant time-dependent differences in navigation patterns during the first 2 minutes of the tours. Subsequently, there are no significant differences in navigation in physical and virtual museums. These findings support the use of immersive virtual environments as empirical tools in human behavioural research at navigation level. The latest generation HMDs show a high degree of presence. There are significant differences in navigation patterns during the first 2 minutes of a tour. Adaptation time need to be considered in future research. Training rooms need to be realistic, to avoid the ‘wow’ effect in the main experiment. Results support the use of Virtual Reality and the latest HMDs as empirical tools in human behavioural research at navigation level.
Imbalanced Remote Sensing Ship Image Classification

Aiming at the unbalanced classification problem of remote sensing ship image datasets in ship target classification and the problem that the traditional decision tree classification algorithm needs to rely on artificial construction features to realize classification, a weighted deep neural decision forest is proposed. This method combines deep learning with resampling. The results show that the method can achieve a better classification accuracy than the traditional decision tree on unbalanced classification of ship target
Efficient design of chaos based 4 bit true random number generator on FPGA

True random number generator is a basic building block of any modern secure communication and cryptography system. FPGA implementation of any system has a flexible architecture and low-cost test cycle. In this paper, we present an FPGA implementation of a high speed true random number generator based on chaos oscillator which gives optimize ratio of bit rate to area. The proposed generator is faster and more compact than the existing chaotic oscillator based TRNGs. The Experimental result shows that the proposed TRNG gives 1439 Mbps with optimizing the use of LUTs and registers. It is verified that the generator passes all the NIST SP 800-22 tests. The proposed TRNG is implemented in two FPGA families Nexus 4 (Artix 7) DDR XC7A100TCSG-1 and Basys 3 XC7A35T1CPG236C (Artix 7) using Xilinx Vivado v.2017. 3 design suite.
Data-Driven Quality of Service Improvements in Hospitals

DONGQING YU: Data-Driven Quality of Service Improvements in Hospitals (Under the direction of Haipeng Shen and Serhan Ziya) In recent years, there has been an increasing interest in developing novel methods for effective and efficient healthcare service delivery and data analytics has widely been recognized as being essential for decision-making at various healthcare service settings. This dissertation consists of three research projects each aiming to improve decisions at three different healthcare settings with one being related to critical care delivery and the other two being related to inpatient patient flow management. Each project combines statistical analysis of hospital data with techniques and methodologies from operations research. The first project concerns care delivery in the cardiac intensive care unit (CICU). We analyze a prospective study to describe admissions and care practices within the CICU of the UNC Hospital, and also evaluate the influence of an open versus closed model of care on patient outcomes and resource consumption. The second and third projects study management of patient flow from emergency department (ED) to hospital internal wards (IWs). Both projects develop effective inpatient flow management policies with the objective of reducing ED boarding time, which is defined as the time between the decision for admission for an ED patient and the time the patient is physically admitted to an IW. Delayed admission to IWs has been identified as a key factor for ED overcrowding and is a big challenge for many hospitals. We approach the problem from two different angles: early discharging patients to free up beds in IWs, and early requesting beds for ED patients based on early prediction of need for IW beds. In both projects, we develop relevant statistical models for analyzing the detailed hospital patient flow data and build mathematical decision models to develop new methods. We also build simulation models and use these models to investigate the benefits of the proposed methods. Simulation studies suggest significant potential improvements in various performance measures of interest.
A 24-GHz DCO With High-Amplitude Stabilization and Enhanced Startup Time for Automotive Radar

In this paper, the optimized design strategies for the implementation of a CMOS digitally controlled oscillator (DCO) are investigated. Moreover, the boosting mechanism for a DCO with and without negative resistance is considered. The proposed design methodology is based on an in-depth mathematical analysis of the startup condition and amplitude of oscillation. This approach results in an optimized topology for a Colpitts Clapp-DCO (CC-DCO). The improved performance is achieved through the negative resistance boosting mechanism. The negative resistance enhances the startup time and increases amplitude stabilization across a wide tuning range (TR). Moreover, it improves the phase noise (PN) performance while suppresses the amplitude-to-phase conversion. The proposed 24-GHz CMOS enhanced CC-DCO (ECC-DCO) is implemented in 65-nm TSMC CMOS process. It can effectively reduce the startup time by 41%. Also, it boosts and stabilizes the amplitude across a TR of 29%. The amplitude varies by 1.5% across the 22–29-GHz TR. The ECC-DCO consumes 12.8 mW. It shows a PN of −106 dBc/Hz at 1-MHz offset frequency and achieves −185-dBc/Hz figure of merit (FoM) and −194-dBc/Hz FoM for tuning.
Computational Design of Auxetic Shells

Recent advances in material science and digital fabrication provide promising opportunities for product design, mechanical and biomedical engineering, robotics, architecture, art, and science. Engineered materials and personalized fabrication are revolutionizing manufacturing culture and having a significant impact on various scientific and industrial works. As new fabrication technologies emerge, effective computational tools are needed to fully exploit the potential of digital fabrication. This thesis introduces a novel computational method for design and fabrication with auxetic materials. The term auxetic refers to solid materials with negative Poisson ratio — when the material is stretched in one direction, it also expands in all other directions. In particular, we study 2D auxetic materials in the form of a triangular linkage which exhibits auxetic behavior at the macro scale. This stretching, in turn, allows the flat material to approximate doublycurved surfaces, making it attractive for fabrication. We physically realize auxetic materials by introducing a specific pattern of cuts into approximately inextensible material such as sheet metal, plastic, or leather. On a larger scale, we use individual rigid triangular elements and connect them with joints. First, this thesis focuses on a regular triangular linkage. When deformed into a curved shape, the linkage yields spatially-varying hexagonal openings. However, the global coupling of the linkage elements makes manual, incremental approach unlikely to succeed when trying to approximate a given curved surface. Thus, we leverage conformal geometry to enable complex surface design. In particular, we compute a global conformal map with bounded scale factor to initialize an otherwise intractable non-linear optimization. Constraint-based optimization is used to find the final linkage configuration that closely approximates a target 3D surface. Furthermore, we develop a computational method for designing novel deployable structures via programmable auxetics, i.e., spatially varying triangular linkage optimized to directly and uniquely encode the target 3D surface in the 2D pattern. The target surface is rapidly deployed from a flat initial state via inflation or gravitational loading. The thesis presents both inverse and forward design tools for interactive surface design with programmable auxetics. This allows the user to efficiently approximate a given shape and directly edit and adapt the auxetic linkage structure to explore the design alternatives. In addition, our solution enables simulation-based form-finding that uses deployment forces for interactive exploration of feasible shapes. The resulting designs can be easily manufactured via digital fabrication technologies such as laser cutting, CNC milling, or 3D printing.
Parametric Study of Uniform Heat Sink Design

Received: 12 January 2019 Accepted: 23 March 2019 The aim of this study is to analyze a single component heat sink design numerically and analytically. The analysis included various modeling assumptions: lumped parameter; one dimensional and two dimensional models. It is shown that the lumped model for the considered problem is capable to accurately predict the steady state temperature of the heat sink with a small discrepancy at the center of the heat sink. Some results are presented graphically for various parameters to show their effect on the steady state temperature of the heat sink including power input; convective heat transfer coefficient; ambient temperature; thickness of the heat sink and the shape of the cross section of the heat sink (circular, ellipsoidal, squared). A few particular applications include single solar cell under high concentration; thermoelectric cooling device and/or high-power led. The findings of the research are twofold: educational and the derived formulae serve as a quick estimate tool to predict the effect of the various parameters on the heat sink temperature and thus aids to estimate the thermodynamic efficiency of the system.
A Robust and Sparse Process Fault Detection Method Based on RSPCA

As a method widely used in fault detection, principal component analysis (PCA) still has challenges in applicability due to its sensitivity to outliers and its difficulty in principal components (PCs) interpretation. In this paper, a robust sparse PCA (RSPCA) model is proposed to improve the robustness and interpretability of the PCA-based fault detection methods. Specifically, better robustness is achieved through capturing the maximal L1-norm variance while the sparse non-zero loadings are given for the PCs to achieve improved interpretability. A fault detection method is developed based on the RSPCA model, which uses the Genetic Algorithm (GA) to determine the number of non-zero loadings in each PC via the variance-sparsity tradeoff and defines $T^{2}$ and SPE statistics based on the selected PCs. In addition, an outlier removing strategy is proposed based on the SPE statistic, which can achieve further improvement to the robustness of the proposed method. The effectiveness and efficacy of the proposed method is evaluated by applying it to both two numerical simulation examples and the Tennessee Eastman (TE) process.
LENTA: Longitudinal Exploration for Network Traffic Analysis From Passive Data

In this paper, we present longitudinal exploration for network traffic analysis (LENTA), a system that supports the network analysts in the identification of traffic generated by services and applications running on the Web. In the case of URLs observed in operative network, LENTA simplifies the analyst’s job by letting her observe few hundreds of clusters instead of the original hundred thousands of single URLs. We implement a self-learning methodology, where the system grows its knowledge, which is used in turn to automatically associate traffic to previously observed services, and identify new traffic generated by possibly suspicious applications. This approach lets the analysts easily observe changes in network traffic, identify new services, and unexpected activities. We follow a data-driven approach and run LENTA on traces collected both in ISP networks and directly on hosts via proxies. We analyze traffic in batches of 24-h worth of traffic. Big data solutions are used to enable horizontal scalability and meet performance requirements. We show that LENTA allows the analyst to clearly understand which services are running on their network, possibly highlighting malicious traffic and changes over time, greatly simplifying the view and understanding of the network traffic.
SHREC'19 track: Feature Curve Extraction on Triangle Meshes

This paper presents the results of the SHREC'19 track: Feature curve extraction on triangle meshes. Given a model, the challenge consists in automatically extracting a subset of the mesh vertices that jointly represent a feature curve. As an optional task, participants were requested to send also a similarity evaluation among the feature curves extracted. The various approaches presented by the participants are discussed, together with their results. The proposed methods highlight different points of view of the problem of feature curve extraction. It is interesting to see that it is possible to deal with this problem with good results, despite the different approaches.
A Q-Learning Approach With Collective Contention Estimation for Bandwidth-Efficient and Fair Access Control in IEEE 802.11p Vehicular Networks

Vehicular ad hoc networks (VANETs) are wireless networks formed of moving vehicle stations, that enable safety-related packet exchanges among them. Their infrastructure-less, unbounded nature allows the formation of dense networks that present a channel sharing issue, which is harder to tackle than in conventional WLANs, due to fundamental differences of the protocol stack. Optimizing channel access strategies is important for the efficient usage of the available wireless bandwidth and the successful deployment of VANETs. We present a Q-learning-based approach to wirelessly network a big number of vehicles and enable the efficient exchange of data packets among them. More specifically, this work focuses on a IEEE 802.11p-compatible contention-based medium access control protocol for efficiently sharing the wireless channel among multiple vehicular stations. The stations feature algorithms that “learn” how to act optimally in a network in order to maximize their achieved packet delivery and minimise bandwidth wastage. Additionally, via a collective contention estimation mechanism, which we embed on the Q-learning agent, faster convergence, higher throughput, and short-term fairness are achieved.
Comparison of ANN and SVM for classification of eye movements in EOG signals

Nowadays, electrooculogram is regarded as one of the most important biomedical signal in measuring and analyzing eye movement patterns. Thus, it is helpful in designing EOG-based Human Computer Interface (HCI). In this research, electrooculography (EOG) data was obtained from five volunteers. The (EOG) data was then preprocessed before feature extraction methods were employed to further reduce the dimensionality of data. Three feature extraction approaches were put forward, namely statistical parameters, autoregressive (AR) coefficients using Burg method, and power spectral density (PSD) using Yule-Walker method. These features would then become input to both artificial neural network (ANN) and support vector machine (SVM). The performance of the combination of different feature extraction methods and classifiers was presented and analyzed. It was found that statistical parameters + SVM achieved the highest classification accuracy of 69.75%.
Spatially Coupled Codes in Turbo Equalization

In this thesis we investigate the application of spatial coupling in turbo equalization. We show that with spatial coupling we can have a fresh perspective on the trade-off between performance in the waterfall and error floor region often encountered in the choice of codes for turbo equalization. Describing turbo equalization in the perspective of message passing on factor graphs, we show various ways of introducing memory in the encoding process and compare the performance improvement through simulations. We also discuss an effective way of decoding such spatially coupled system by using window decoding in order to minimize the decoding latency and complexity. We manged to show that with spatial coupling we can have it both ways; that is, have a good performance in the waterfall region while having low error floors. (Less)
Construction of Large Scale Isolated Word Speech Corpus in Bangla

A new speech corpus of isolated words in Bangla language has recorded including high frequent words from a text corpus BdNC01. It has designed specifically for various research activities related to speaker-independent Bangla speech recognition. The database consists of speech of 100 speakers, each of them speaking 1081 words. Another 50 new speakers were employed to speak all the list of words to construct a test database. Every utterance was repeated five times in different days to avoid time variation of speaker property. The total 375 hours of original recording makes the corpora largest in its type, size and language domain. This paper describes the motivation for the corpora and the processes undertaken in its construction. The paper concludes with the usability of the corpus.
Technical Note with Supporting Information for Bucket Wheel Reclaimer Extrinsic Parameter Calibration

This note provides additional information and derivations that are referred to in [1]. The main article should be read first. This technical note includes materials that could not fit within the journal page constraints. The note is organized as follows. Section I explains the plane parameter extraction method stated in Section VI-C of [1]. Section II provides derivation of eqn. (15) of [1]. Section III provides derivation of eqn. (20) of [1].
Resource Allocation Approaches for Two-Tiers Machine-to-Machine Communications in an Interference Limited Environment

One of the main challenges of supporting massive machine-type-communications (MTCs) by future mobile networks, is the radio access network (RAN) congestion. A viable solution is a hierarchical architecture where cluster-grouped machines are indirectly connected to the mobile network through a cluster gateway. This paper considers such an architecture and focuses on radio resource allocation to machine-clusters where resources can be spatially reused for concurrent transmissions. In particular, resources can be accessed simultaneously by machines of different clusters and by different communication phases, thus resulting in an interference-limited system. Three different resource partitioning schemes are proposed and evaluated, mainly differing on how interference is taken into account. The aim is to minimize the packet loss probability, and thus maximizing the spectral efficiency. Numerical results show the effectiveness of the proposed schemes in comparison to benchmark methods. In particular, by joining resource reuse and interference-mitigation, the efficiency improves significantly.
CNN-LSTM Combined Network for IoT Enabled Fall Detection Applications

An accidental fall could do a great damage to the health of elderly. Failure to provide timely assistance after a fall may cause injury or even death. In this paper, a fall detection algorithm based on Convolutional Neural Network (CNN)-Long Short Term Memory (LSTM) combined network is proposed, which makes full use of the powerful feature extraction ability of CNN and the excellent time series processing ability of LSTM. Data required by the algorithm is only the resultant acceleration from a low cost three-axis acceleration sensor. The experimental results show that compared with the algorithms based on Support Vector Machine (SVM) and CNN, the proposed algorithm has higher detection accuracy with a small data volume, which is very suitable for Internet of Things (IoT) enabled fall detection applications.
A Mixed-Signal Circuit Technique for Cancellation of Multiple Modulated Spurs in 4G/5G Carrier Aggregation Transceivers

In carrier-aggregation 4G/5G transceivers local-oscillator spurs are generated which may down-convert any interferer located at the spurious frequencies. A mixed-signal circuit recreates the spur building laws by multiphase mixing and provides replica of multiple blocker-induced distortions without prior knowledge of their carrier frequencies. This technique, implemented in 28-nm CMOS, can cancel multiple spurious receive products by up to 33 dB for blocker power levels of up to −10 dBm at the LNA input.
The role of Internet of Things in delivering smart construction

The construction industry is the least digitised sector in the world and it contributes significantly less, in terms of productivity, to the global economy than its average potential. Current trends in the construction industry are aimed at leveraging cutting-edge technologies to improve productivity and decision making, this approach is commonly referred to as smart construction. Smart construction involves the augmentation of construction resources such as machinery, devices, components and people with digital technologies for transforming the construction industry. Many aspects of construction have witnessed transformation through automation, cyber-physical systems, cloud- computing, cognitive computing, immersive technologies and so on. An emerging concept in digital technology known as internet of things (IoT) is gradually advancing the possibilities of smart construction in various areas of application. This study aims to assess the role of internet of things (IoT) in enhancing the delivery of smart construction. From the perspectives of modern construction methods such as offsite construction, lean construction, smart assembly and so on, this study presents a mixed methods systematic review of the various areas of application of IoT in modern construction methods and identifies the emerging themes of IoT application in smart construction. The methods and current challenges of using IoT in application areas such as; cyber-physical modelling; offsite manufacturing; onsite assembly; handling and logistics; real-time monitoring and control; health and safety; information management and communication; energy management; waste minimisation; remote inspection; and lean construction management, is presented in the paper. This study substantially contributes to existing knowledge of using digital technologies for transforming construction operations and lays a solid foundation for further research in the application of IoT for smart construction. The challenges identified from the various areas of IoT application are opportunities for the development of cutting-edge solutions in construction.