Miracle-dz/UniFAR-FTUs huggingface dataset:
Player Assignment in MEC Gaming for Social Interactivity and Server Provisioning Optimization

In Mobile Edge Computing (MEC), edge servers are deployed on Base Stations (BSs) to provide high accessibility to nearby users, alleviating the computational burden of the remote cloud and significantly reducing network delays. Still, for distributed interactive applications (DIAs), such as cloud games, where players team up and form social communities, one fundamental challenge is the discovery of the most suitable player-to-server assignment, that achieves a balanced tradeoff between low-delay social interactivity and low-cost server provisioning, under strict proximity and capacity constraints. In this paper, we formally model the problem via mixed-integer linear programming. However, its solution is complex falling under the category of existing capacitated facility location problems. Thus, we present the “Interactivity-based Edge Server Allocation” (IESA) algorithm, a novel heuristic approach to efficiently assign and then iteratively improve the player-to-server allocation. IESA is evaluated using trace-driven simulations that demonstrate how it outperforms the baseline and state-of-the-art assignment alternatives.
A Systematic Review of Wearable Devices for Orientation and Mobility of Adults With Visual Impairment and Blindness

Wearable devices have been developed to improve the navigation of blind and visually impaired people. With technological advancements, the application of wearable devices has been increasing. This systematic review aimed to explore existing literature on technologies used in wearable devices to provide independent and safe mobility for visually impaired people. Searches were conducted in six electronic databases (PubMed, Web of Science, Scopus, Cochrane, ACM Digital Library and SciELO). Our systematic review included 61 studies. The results show that the majority of studies used audio information as a feedback interface and a combination of technologies for obstacle detection - especially the integration of sensor-based and computer vision-based technologies. The findings also showed the importance of including visually impaired individuals during prototype evaluation and the need for including safety evaluation which is currently lacking. These results have important implications for developing wearable devices for the safe mobility of visually impaired people.
Vision systems-an overview

Vision is one of the most difficult core technologies, but is essential for robotic automation. Human vision is so natural that it is hard to appreciate the complexities of creating it artificially, but vision is the only thing holding back the development of truly autonomous machine systems. Machine vision systems are designed to eliminate or reduce human observation and error, such as NASA's Mars explorers being deployed in the late 1990's or Meta Machines Ltd.'s vision system that can withstand the high temperatures encountered during high-current welding. Through research into finding better algorithms and more sensitive cameras, the number of potential applications will increase due to the decrease in price. These applications fall into three categories: measurement, inspection and guidance. Measurement systems find the dimensions of an object through digitization and manipulation of the object's picture. Inspection systems determine whether an object matches a preset description. Guidance systems cause a machine to perform certain actions based on what it "sees". Vision research is difficult because the reasoning the human brain applies to identify objects cannot be easily transferred to a computer. Computer vision work traditionally has been based on statistical and heuristics, or trial and error methods to analyze image data. Common methods, such as edge detection, give unreliable information about the scene being viewed.
Business Processes Improvement in ANA, SA, Aeroportos de Portugal: Maintenance Management Procedure

With the implementation of a computerized maintenance management system for a corporate perspective in the Organization ANA, SA. Aeroportos de Portugal, combined with several parameterizations related to core business processes in place, naturally there was a set of opportunities to improve business processes through research and metrics development allowing more analyses. These were previously dealt without the same depth and without access to the same database and data model. On this basis, the idealization of the maintenance management procedure, based on the framework for maintenance management in force in the organization, moved quickly to the reality. The development of the procedure starts by identifying problems, setting goals and objectives to be achieved, research literature in maintenance area and best practice in service management with a focus on ITIL, study existing maintenance standards with respect to concepts and KPI calculation methods, presentation of business processes in production at the Organization, development of the procedure for maintenance management with appropriated methods and outcome analysis, and finally the main conclusions of the work. A major contribution of this work has been considered a series of actions to improve and/or correct existing business processes that may prove technical, organizational and economic benefits for the maintenance management in that Organization.
Flexible V-band mmWave Analog-RoF Transmission of 5G and WiGig signals using an InP-SiN Integrated Laser Module

In this paper, we have demonstrated analog radio-over-fiber (A-RoF) transmission of 5G (256/512-QAM) and WiGig (64-QAM) signals in the millimeter-wave (mm-wave) frequency range from 55 GHz - 65 GHz in a system with 10 km fiber and 1 m wireless channels. The system employs a hybrid integrated InP-Si3 N4 dual laser module for flexible heterodyne operation in the V-band and a simplified system setup, with respect to our prior demonstrations. The experimental results show excellent performance with bit error ratio (BER) values as low as of $8.46 \times 10^{-6}$ and $1.11 \times 10^{-4}$, and lowest error vector magnitudes (EVMs) of 2.4 % and 6.1%, achieved for the 5G and WiGig signals, respectively.
Fiber-optic switch arrays for optical subscriber transmission systems

Optical array devices play an important role in subscriber transmission systems because they can efficiently accommodate the large number of optical channels required. The use of two single-mode fiber-optic switch arrays in subscriber transmission systems is discussed. The first is a 16-element micromechanical switch array which is electrostatically driven and is used to separate abnormal subscriber units that transmit disturbing light signals. The second is a 16-element switch array which interconnects a pair of probe fibers and any one of 16-channel subscriber-line fibers to execute fault testing. >
Lecturers’ Intention to Use Online Video Conferencing tools: The role of Perceived severity

The Covid-19 pandemic has profoundly affected all life activities, including training activities at higher education institutions. However, teaching and learning activities can continue through Online Video Conferencing tools as Google Meet, Zoom, Microsoft Team. This study explores the effect of the Perceived severity of the Covid-19 pandemic as an antecedent of the Technology Acceptance Model. The study surveyed 361 lecturers at higher education institutions in Vietnam. The research results confirmed the results of the Technology Acceptance Model as using Online Video Conferencing tools and discovered the positive influence of Perceived severity of Covid-19 pandemic on perceived usefulness and ease of use. Thereby, the study proposed some administrative implications to improve the efficiency of using Online Video Conferencing tools.
Design and Experiments of a High-Conversion-Efficiency

A high-efficiency rectenna element has been designed and tested at 5.8 GHz for applications involving microwave- power transmission. The dipole antenna and filtering circuitry are printed on a thin duroid substrate. A silicon Schottky- barrier mixer diode with a low breakdown voltage is used as the rectifying device. The rectenna element is tested inside a waveguide simulator and achieves an RF-to-dc conversion efficiency of 82% at an input power level of 50-mW and 327- load. Closed-form equations are given for the diode efficiency and input impedance as a function of input RF power. Measured and calculated efficiency results are in good agreement. The antenna and circuit design are based on a full-wave electromagnetic simulator. Second harmonic power levels are 21 dB down from the fundamental input power.
Soundfield Reconstruction in Reverberant Rooms Based on Compressive Sensing and Image-Source Models of Early Reflections

The estimation of the soundfield in locations different from the measurement points in a room is a complex problem widely discussed in the literature. One of the key challenges in virtual acoustics is soundfield reconstruction in environments characterized by nearfield sources and strong reverberation. Most of the existing solutions are computationally expensive and they often just achieve the reconstruction of the direct soundfield. Considering a sparse distribution of acoustic sources in a room and a compressive sensing framework, in this work, we propose a method that targets the reconstruction of both the direct and the reverberant soundfield by explicitly modeling early reflections as near-field sources. We show how, by exploiting some loose prior knowledge on the position of the source and the geometry of the environment, the computational complexity can be reduced, while ensuring robustness to errors in the prior knowledge. The proposed method is validated through simulations.
Simultaneous Notch Filtering and Linearization in an Integrated Microwave Photonic Circuit

We demonstrated, for the first time, an integrated microwave photonic (MWP) circuit exhibiting simultaneous tunable notch filtering and linearization, leading to a high performance filter with enhanced linearity and spurious free dynamic range (SFDR). We unlock this unique feature by manipulating the phase and amplitude of multi-order RF modulation sidebands using a versatile MWP spectral shaping circuit. Through a series of experiments, we show a linearized notch filter that exhibits a stopband rejection of 53 dB, more than 32 dB reduction of third-order intermodulation distortion in the passband, and an enhanced SFDR of 119 dB$\cdot Hz^{2/3}$. Our demonstration breaks the conventional and fragmented approach of functionality and linearization in integrated circuit and will potentially stimulate the implementation of integrated MWP system with advanced functionalities and high performance in the future.
Pictures as a Form of Protest: A Survey and Analysis of Images Posted During the Stop Asian Hate Movement on Twitter

Modern protests are not limited to on-the-ground operations, and the ease and speed at which users can upload images to social media platforms has enabled protests to manifest online. Previous analysis of protest imagery from social media sites categorized these images into groups including texts, screenshots, memes, and artwork. However, large-scale manual annotation to identify different types of images is not feasible. By applying machine learning to a large Twitter dataset focused on the Stop Asian Hate movement, we found the type of image an account posted during protests on Twitter is tied to the credibility and political leaning of posted content, type of witnessing (remote or connective), and community formation.
Global Maximum Power Point Tracking Algorithm Based on Model Calculation for Photovoltaic Array

Partial shading is an unavoidable problem that results in multiple peaks in output characteristics of photovoltaic array, which brings a challenge to the maximum power tracking method. Most of the existing methods need to sample the full or part of the voltage range to obtain the global maximum power point, resulting in slow tracking speed and power loss during sampling. Therefore, this paper proposes a global maximum power point tracking strategy based on model calculation from the perspective of directly determining the optimal output voltage. Based on the analysis of the output characteristics of photovoltaic array under partial shading conditions, this paper establishes a new mathematical model to calculate the voltage and power at each power peak point. When the environmental conditions change, the voltage at the global maximum power point can be directly obtained according to the established mathematical relationship, so that the global maximum power point can be quickly reached by directly controlling the output voltage. To verify the performance of the proposed method, simulations have been carried out in Simulink environment.
Knowing What You Know

To help operators handle increasing task demands, automated systems are increasingly being used in modern work settings. However, a potential consequence of such systems is the out of the loop performance syndrome, which refers to the reduced ability of operators working with high levels of automation support to take over manual control in the event of an automation failure (e.g., Wiener & Curry, 1980). One of the main factors considered responsible for this is a loss of operator situation awareness (SA; Endsley & Kiris, 1995). In order to improve SA and consequently improve performance, it becomes important to better understand the process of SA and the underlying factors that predict SA (e.g., Durso & Sethumadhavan, 2008). The role of meta-SA in predicting SA when individuals control air traffic with automation support is investigated in this paper. Results have practical implications for the development of SA training programs.
Compact sectoral fractal planar monopole antenna for wideband wireless applications

A compact size sectoral fractal planar monopole antenna is proposed for wideband wireless application. The feed circuit is a 50‐Ω microstrip line with a matching section over a rectangular ground plane. The measured −10 dB reflection bandwidth for frequency range (3.22–6.55 GHz) is 68.04% covering WiMAX (3.4–3.6 and 3.7–4.2 GHz), Wi‐Fi 802.11y (3.6–3.7 GHz), and WLAN 802.11 (5.31–6.32 GHz). This antenna has acceptable agreement is obtained for the simulated and measured antenna performance parameters. © 2014 Wiley Periodicals, Inc. Microwave Opt Technol Lett 56:1073–1076, 2014
Systems Neuroengineering: Understanding and Interacting with the Brain

In this paper, we review the current state-of-the-art techniques used for understanding the inner workings of the brain at a systems level. The neural activity that governs our everyday lives involves an intricate coordination of many processes that can be attributed to a variety of brain regions. On the surface, many of these functions can appear to be controlled by specific anatomical structures; however, in reality, numerous dynamic networks within the brain contribute to its function through an interconnected web of neuronal and synaptic pathways. The brain, in its healthy or pathological state, can therefore be best understood by taking a systems-level approach. While numerous neuroengineering technologies exist, we focus here on three major thrusts in the field of systems neuroengineering: neuroimaging, neural interfacing, and neuromodulation. Neuroimaging enables us to delineate the structural and functional organization of the brain, which is key in understanding how the neural system functions in both normal and disease states. Based on such knowledge, devices can be used either to communicate with the neural system, as in neural interface systems, or to modulate brain activity, as in neuromodulation systems. The consideration of these three fields is key to the development and application of neuro-devices. Feedback-based neuro-devices require the ability to sense neural activity (via a neuroimaging modality) through a neural interface (invasive or noninvasive) and ultimately to select a set of stimulation parameters in order to alter neural function via a neuromodulation modality. Systems neuroengineering refers to the use of engineering tools and technologies to image, decode, and modulate the brain in order to comprehend its functions and to repair its dysfunction. Interactions between these fields will help to shape the future of systems neuroengineering—to develop neurotechniques for enhancing the understanding of whole-brain function and dysfunction, and the management of neurological and mental disorders.
Data driven models of human shape, pose and garment deformation

This thesis addresses the problem of modeling human shape in three dimensions. Specifically, this thesis is focused on modeling body shape variation across multiple individuals, pose induced shape deformations and garment deformations that are influenced both by body shape and pose. A methodology for constructing data driven models of human body and garment deformation is provided. Additionally, an application for online fashion retailing is presented. Abstract Firstly, a quantitative and qualitative evaluation, is introduced, of surface representations used in recent statistical models of human shape and pose. It is shown that the Euclidean representation generates a more compact human shape model compared to other representations. A small number of model parameters indicates better convergence in a human body estimation framework. In contrast, a high number of model parameters increases the risk of the optimization getting trapped in a local optimum. Based on these insights a system for human body shape estimation and classification for on-line fashion applications is presented. Given a single image of a subject and the subject's height and weight the proposed framework is able to estimate the 3D human body shape using a learnt statistical model. Results demonstrate that a single image holds sufficient information for accurate shape classification. This technology has been exploited as part of a collaborative project with fashion designers to develop a mobile app to classify body shape for clothing recommendation in online fashion retail. Abstract Next, Shape and Pose Space Deformation (SPSD) is presented, a technique for modeling subject specific pose induced deformations. By exploiting examples of different people in multiple poses, plausible animations of novel subjects can be synthesized by interpolating and extrapolating in a joint shape and pose parameter space. The results show that greater detail is achieved by incorporating subject specific pose deformations as opposed to a subject independent pose model. Finally, SPSD is extended to a three layered data-driven model of human shape, pose and garment deformation. Each layer represents the deformation of a template mesh and can be controlled independently and intuitively. The garment deformation layer is trained on sequences of dressed actors and relies on a novel technique for human shape and posture estimation under clothing.
Automating the Service Function Chain Availability Assessment

Modern telecommunications networks are characterised by increased dynamism and heterogeneity from the cloud to the edge. To meet these requirements and remain competitive, network operators use software defined networking (SDN) and network functions virtualisation (NFV) to make networks programmable, flexible, and agile. Service function chaining (SFC) enables NFV through the ordered sequencing of VNFs and subsequent steering of flows through them to provide end-to-end services. SFCs must meet availability constraints while avoiding resource wastage due to unnecessary redundancy. This paper proposes a novel approach to evaluate the availability of SFC placement considering the associated redundancy strategies using Stochastic Petri Net (SPN) models. We propose an algorithm to generate and resolve relatively small SPN models for SFC availability in an automatic way and then compare baseline SPN models with this algorithm across five SFC placement scenarios. In all scenarios, the proposed algorithm outperformed baseline SPN models in runtime performance.
Utilization of Convolutional Neural Networks for Urinary Bladder Cancer Diagnosis Recognition From CT Imagery

In this paper, an approach for urinary bladder cancer diagnosis from computer tomography (CT) images based on the application of convolutional neural networks (CNN) is presented. The image data set that consists of three main parts (frontal, horizontal, and sagittal plane) is used. In order to classify images, pre-defined CNN architectures are used. CNN performances are evaluated by using 5-fold cross-validation procedure that gives information about classification and generalization performances. From the presented results, it can be noticed that higher performances are achieved if more complex CNN architectures are used. Higher performances can be noticed regardless of a plane in which images are captured. An increase in performances can be noticed in both classification and generalization context.
Computer Modeling of Radiofrequency Cardiac Ablation including Heartbeat-Induced Electrode Displacement

Background The state of the art in computer modeling of radiofrequency catheter ablation (RFCA) only considers a static model, i.e. it does not allow modeling ablation electrode displacements induced by tissue movement due to heartbeats. This feature is theoretically required, since heartbeat-induced changes in contact force can be detected during this clinical procedure. Methods We built a 2D RFCA model coupling electrical, thermal and mechanical problems and simulated a standard energy setting (25 W – 30 s). The mechanical interaction between the ablation electrode and tissue was dynamically modeled to reproduce heartbeat-induced changes in the electrode insertion depth from 0.86 to 2.05 mm, which corresponded with contact forces between 10 and 30 g when cardiac tissue was modeled by a hyperelastic Neo-Hookean model with a Young’s modulus of 75 kPa and Poisson’s ratio of 0.49. Results The dynamic model computed a lesion depth of 5.86 mm, which is within the range of previous experimental results based on a beating heart for a similar energy setting and contact force (5.6−6.7 mm). Lesion size was practically identical (differences less than 0.02 mm) to that using a static model with the electrode inserted to an average depth (1.46 mm, equivalent to 20 g contact force). Conclusions The RFCA dynamic model including heartbeat-induced electrode displacement predicts lesion depth reasonably well compared to previous experimental results based on a beating heart model, however this is true only at a standard energy setting and moderate contact force.