Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of annealing temperature on structural and bonded states of titanate nanotube films

A conversion from commercial titania (TiO2) nanoparticles to nanotubes was achieved by a hydrothermal method. The titanate nanotube (titanate) film was then deposited on a Si (001) substrate using an electrophoretic deposition (EPD) technique. The post hydrothermal treatment was then carried out by annealing the films at 300–1000°C for 30min in the static air. A major amount of intercalated sodium (Na) in as-synthesized titanate nanotubes was removed during the electrodeposition process. The collapse of the tubular structure can be seen clearly when annealed above 500°C. X-ray diffraction data indicate a significant increase in the anatase phase peak intensity with annealing temperature. O 1s peak is found to be built up of subpeaks of H2O, −OH, and Ti–O. Annealing results in an increase of the Ti–O peak intensity while other peaks disappear. Clear changes in the O 1s peak positions, symmetry, and shift towards lower energy (0.8eV) are evident with the increasing annealing temperature. The doublet spectra.
Preliminary Viability Test of a 3-D-Consumer-Camera-Based System for Automatic Gait Feature Detection in People with and without Parkinson’s Disease

Movement contains a large amount of cognitive, central-motor and musculoskeletal information that correlates directly with the general state of health. Approaches to automatic movement analysis, in particular to automatized gait analysis are of utmost relevance to offer modern technological support for health professionals. The purpose of this paper is to present the experimental setup and preliminary results of the viability test of the TEDIPA study, which will evaluate the comparability of Azure Kinect’s consumer depth and color camera with the medical product for gait analysis GAITRite, regarding accuracy and precision when measuring key indicators of healthy and pathological gait.
The Memristor Laser

Here we demonstrate the first-ever semiconductor laser with non-volatile tuning functionality. A GaAs-on Silicon quantum dot laser is integrated with an Al2O3 memristor, which results in lasing mode being manipulated by memristor states. The memristor controls the microring laser output wavelength in a non-volatile manner.
Smart Parking with Learning-aided User Activity Sensing Based on Edge Computing

Parking is an everyday ordinary activity in urban lives. In other words, it is the most common and essential requirement of all drivers in a car park to fast search a preferred parking spot. Recently most parking lots provide a sensing-based display system to notify available parking spots. However, such systems are still unable to tell drivers exact parking spots and make any recommendation to improve the traffic conditions and driver experiences. In this paper, a novel machine-learning-aided smart parking system based on Internet of Things, smart mobile devices and edge computing is proposed. This novel parking system aims at providing customized parking experience to users through highly accurate positioning and user activity sensing computed as the edge intelligence. The proposed system mainly focuses on increasing user experience for parking through the concatenated autoencoder design for improving performances of the user positioning mechanism.
Mobility Aware and Dynamic Migration of MEC Services for the Internet of Vehicles

Vehicles are becoming connected entities, and with the advent of online gaming, on demand streaming and assisted driving services, are expected to turn into data hubs with abundant computing needs. In this article, we show the value of estimating vehicular mobility as 5G users move across radio cells, and of using such estimates in combination with an online algorithm that assesses when and where the computing services (virtual machines, VM) that are run on the mobile edge nodes are to be migrated to ensure service continuity at the vehicles. This problem is tackled via a Lyapunov-based approach, which is here solved in closed form, leading to a low-complexity and distributed algorithm, whose performance is numerically assessed in a real-life scenario, featuring thousands of vehicles and densely deployed 5G base stations. Our numerical results demonstrate a reduction of more than 50% in the energy expenditure with respect to previous strategies (full migration). Also, our scheme self-adapts to meet any given risk target, which is posed as an optimization constraint and represents the probability that the computing service is interrupted during a handover. Through it, we can effectively control the trade-off between seamless computation and energy consumption when migrating VMs.
Advances in Research on 300mm Gallium Nitride-on-Si(111) NMOS Transistor and Silicon CMOS Integration

We discuss advances in our research on 300mm GaN NMOS by demonstrating GaN-on-Si(111) NMOS transistors achieving low R<inf>ON</inf>=330Ω-μm; high ID,max=1.7mA/μm; BV<inf>DS</inf> (at I<inf>D</inf>=1μA/μm) of up to 90V with excellent R<inf>ON</inf>=660Ω-μm; record f<inf>T</inf>/f<inf>MAX</inf> of 200/350GHz for GaN-on-Si; industry’s best RF switch R<inf>on</inf>C<inf>off</inf> =55fs; and highest mmwave (28GHz) RF power amplifier peak PAE of 65% @ 19.5dBm saturated power. We discuss and compare the challenges in approaches to GaN and Si CMOS integration research including: (a) poly-silicon CMOS, (b) heterogeneous epitaxy of GaN and Si(111) CMOS, (c) wafer-to-wafer bonding [2], and (d) 3D monolithic Si(100) layer transfer using bonding techniques [1],[3].
Low power consumption and high resolution 1280X960 Gate Assisted Photonic Demodulator pixel for indirect Time of flight

A 1280×960 floating diffusion storage global shutter image sensor is implemented in a 3D stacked back illuminated indirect time of flight sensor (iToF). The sensor, achieves 18,000e- full well capacity and 32% quantum efficiency (QE) with a pyramid surface for diffraction (PSD) structure [1], utilizing a 3. 5µm pixel. Low power consumption is also achieved, due to low leakage current for the iToF pixel and low resistance Cu-Cu connection metal wiring. These device architectures enable high resolution and wide dynamic range 3D depth sensing for both near and far objects.
Optimizing a radial visualization with a genetic algorithm

We consider in this paper a radial visualization called POIViz as a starting point to be improved with an optimization procedure. In our previous work, we studied POIViz and showed that it was able to represent multidimensional data in 2D within a few seconds, even for datasets with millions of records. We provided a simple heuristic to select the Points Of Interest (POIs), i.e., the 2D anchors that determine the layout of the data. In this paper, we extend POIViz to Gen-POIViz by proposing a genetic algorithm (GA) that can greatly optimize the quality of the visualization. The GA searches for a set of POIs that minimizes a cost function that is based on Kruskal’s stress. Furthermore, Gen-POIViz can find relevant POIs with a small sample of the data only, and thus it can compute a projection of the complete data in a very short time. We provide comparative results with standard methods in data projection. Gen-POIViz obtains results with a quality that is between force-directed Multidimensional Scaling (MDS) and Principal Components Analysis (PCA). On larger datasets, we show the advantage of our method when it works on a data sample. It can be much faster than MDS, and it can be run with even larger datasets for which other methods fail.
Motivational design techniques to support healthy eating habits at work

This exploratory study provides a deeper understanding of employees when they are asked to use a personalized meal recommendation application in the workplace. Motivational design techniques were integrated into 25 alternative designs and evaluated. Our initial results show that participants appreciated the designs and highlighted the importance of personalized goals, the option to monitor eating habits, and the need for validated, personalized nutritional information.
A High-Gain Circularly-Polarized Holographic Antenna Based on Impedance-Modulated Surface

A high-gain circularly polarized (CP) holographic antenna (HA) based on impedance-modulated surface (IMS) is proposed in this paper, which is composed of a source antenna and the IMS. A monopole antenna working around 11.4 GHz can drive the IMS. Surface wave generated by the monopole can be converted into directional CP wave through the IMS. The radiation characteristics of the three kinds of phase modulated HAs are compared and 12-phase modulated HA is selected as the proposed antenna at last. The peak gain of the presented CP HA is up to 18.7 dBi at 11.4 GHz. The operating band of the CP HA is from 10.5 to 13.5 GHz (25%). In addition, a good agreement is achieved between simulation and measurement. Moreover, the proposed antenna has the advantages of simple production process and low cost.
Terahertz Deep Learning Super Resolution Imaging Training on Sinogram

We presented a supervised deep learning method to decrease six-fold data acquisition time and to suppress terahertz image artifact levels based on sinogram from terahertz time-domain spectroscopy (THz- TDS). Our method delivered superior image quality than conventional bicubic interpolation method with 0.9612 and 0.0155 in mean square error (MSE) and structural similarity index (SSIM), respectively.
Improving the Signal-to-Noise Ratio on Terahertz Compressive Imaging

we present a compact terahertz compressive imaging utilizing the vector network analyzer base on the compressive sensing technology. To eliminate the noise from the terahertz imaging system and improve the signal-to-noise ratio of the reconstructed image, we propose a novel compressive sensing methodology, in which the Paley-Hadamard sensing matrix and normalization measurement data algorithm are introduced. From the numerical and experimental results, the anti-noise performance of the proposed methodology is superior to the conventional compressive technology.
A Coplanar Dual-Band Antenn with Low Profile

A coplanar linear polarized dual-band antenna with very low profile is proposed in this paper. The evolution of the dipole antenna based on the slotted patch is introduced. The effects of the patch on the central dipole are analyzed in detail. A low profile single polarized dual-band coplanar antenna with wide impedance bandwidths of 21.12% and 61.43% is realized and fabricated. The proposed antenna has peak gains higher than 9.1 dB in the lower band and 7.9 dB in the higher band. The measured isolation between the two ports is greater than 23 dB. It can be applied in low-profile multiband small base stations in the next generation system.
Opportunities and Limitations of Emerging Analog in-Memory Compute DNN Architectures

This paper assesses the benefits and pitfalls of Analog in-Memory Compute (AiMC) solutions from the accelerator level. The study shows that AiMC can improve efficiency significantly, yet only when the AiMC array topology and the memory technology are co-optimized with the memory and system architecture. The paper provides design guidelines to maximally exploit the opportunities of AiMC technology, and gives an outlook towards emerging memory technologies.
Time-Aligned Edge Plots for Dynamic Graph Visualization

We present time-aligned edge plots: time- and edge-scalable representations of dynamic graphs. Vertices are mapped to two vertical parallel axes. The left axis depicts the source vertices, whereas the right one depicts the destination vertices. The time axis is horizontally embedded in-between the two axes, resulting in a two-dimensional graph layout. Edges are added by drawing straight lines connecting the corresponding source and destination vertices through time, while the pixels along the lines are used to encode the time-varying information. In this way, the depiction of edges at the individual timepoints is reduced to only a few pixels, resulting in a less cluttered representation of dynamic graphs, while the alignment of edges over time reveals the temporal patterns in the data and preserves the users’ mental map. We evaluate our approach by comparing it theoretically and empirically against the state-of-the-art using dynamic graphs of varying complexities.
Learning Based CSI Feedback Prediction for 5G NR

Acquisition of accurate channel state information (CSI) is critical in multiple-input and multiple-output (MIMO) systems to achieve efficient link adaptation. As CSI is typically estimated at the receiver, effective and efficient acquisition at the transmitter is challenging. The primary concerns are estimation accuracy, reporting overhead, and channel aging effects caused due to delayed CSI usage. In this paper, we design a deep learning based CSI prediction framework (DCP) to address these challenges. The DCP consists of a channel prediction network to compensate the aging effects, followed by a deep learning based CSI estimator (DCE) for accurate estimation. Through simulations, we show that the DCE can yield up to 15% higher spectral efficiency (SE) due to the improved estimation accuracy while significantly lowering the complexity compared to conventional approaches. Moreover, for the same CSI reporting overhead, DCP can improve the SE by up to 20% over conventional techniques. Further, even at 50% of the CSI reporting overhead, DCP improves the SE by up to 6.5% over the conventional methods.
In-line THz Multiplexer Fabricated by Additive Manufacturing

In this work we present a 3D printed mux/demux device based on rectangular waveguides. The principle of operation of the proposed technology relies on the coupled mode theory. The proposed device is able to merge and separate two independent broadband input channels into a single output channel. This represents an attractive option for novel passive elements able to process broadband THz signals for communication proposes.
A Broadband Circularly Polarized Antenna Fed by Coplanar Waveguide

A novel circular-polarized (CP) antenna, which is fed by a coplanar waveguide is introduced. The structure of antenna is comprised of a specific L-shaped grounded and a T-shaped strips, exhibits left-hand CP (LHCP) at the broadside direction. From the simulation results, it can be seen that from 3.0 to 6.5 GHz the 3 dB axial ratio (AR) bandwidth ranges from 3.46 to 6.10 GHz. The results of test and simulation are basically consistent. The designed antenna has many advantages, such as small size, simple structure and wideband axial ratio.
Modelling Educational Resources to Support Accessibility Requirements

The design and construction of personalized and flexible e-learning systems can benefit from the use of sophisticated knowledge modeling and data-analysis techniques to provide learning resources that are accessible by anyone, including learners with special accessibility needs and preferences. The paper describes a personalized educational resources system that considers learner's needs, especially when it comes to learners with accessibility requirements. We use Semantic Web technologies as resource representation formalism to provide learners with suitable learning objects, learning activities, and teaching methods based on their different preferences and needs. The system supports the creation of interactive semantic queries to bring the result sets closer to user research requirements.
Evaluation of Salvia officinalis L. Essential Oil under Selenium Treatments

Abstract The effects of selenium treatments [0 (control), 2, 4, 6 and 8 mg 1−1] on essential oil extracted from Salvia officinalis L. plants were investigated by GC and GC/MS during two successive seasons 2008 and 2009. Essential oil content (% and g plant −1) increased with various treatments of selenium compared with control. The highest essential oil percentage (0.5%) was recorded at 8 mg 1−1 selenium treatment. Greatest essential oil yields (0.1 g plant−1) were recorded at 6 and 8 mg l−1 selenium treatments in both seasons. Twenty-two constituents were identified in essential oil extracted from Salvia officinalis L. aerial parts, accounting for 98.6–99.9% of total constituents, and belong to eight chemical main classes. Ketones class was the major one. The major constituents of Salvia officinalis L. essential oil were α-, β-thujone and camphor which increased as selenium level increase. The highest amounts of major compounds [α-thujone (55.0%), β- thujone (12.7%) and camphor (31.8%)] resulted from 8 mg l−1 selenium treatment. The chemical classes of essential oil such as monoterpene hydrocarbons, oxygenated monoterpenes, sesquiterpene hydrocarbons and oxygenated sesquiterpenes decreased in all selenium treatments compared with control. However Ketones class increased as selenium level increase. On the other hand, ethers, aldehydes and esters decreased at all selenium treatments except for 2 mg l−1 selenium treatment were increased compared with control.
Deep Learning in Skin Lesion Analysis Towards Cancer Detection

Detecting Melanoma, the deadliest skin cancer, at the early stage can exceptionally escalate the possibility of the cure up to 99.2% 5-year survival rate. Manual examination by the dermatologist continues to be used as the core and most trusted method till today, albeit a decade of effort in using the technology. Therefore, given the low supply of dermatologists, it is impossible to proactively run the surveillance on people at highest risk for early finding. Deep Convolutional Neural Networks (DCNNs) have demonstrated a dramatic breakthrough in automatic skin lesion classification, which is imperative to improve the diagnostic performance over the mass population with limited access to specialists. Web-application based dermoscopic imaging with integrated artificial intelligence (AI) is a plausible accessible method for future skin lesion analysis. The artificially intelligent plugin, consequently, can serve as enablers for the dermatology community. In this paper, we report the findings of our investigation of using DCNNs for automated Melanoma region segmentation in dermoscopy images. For training and evaluation, we use the HAM10000 public dataset. We aim to realize our model by creating a web tool that can tell general practitioners (GP) and lab technologists the probability diagnoses for a given skin lesion. This automation will assist in fast segregation of the high-risk patients and speed up the follow-up diagnosis and treatment workflow.
Worriers versus Warriors: Tailoring mHealth to Address Differences in Patients with Chronic Arthritis

Chronic arthritis (CA) represents a group of musculoskeletal diseases that require frequent physical therapy and a well-monitored medication regime to improve or maintain disease status. To support patients in this disease management, health apps may provide a solution. However, patients with CA differ in their disease trajectory, needs and preferences. There-fore, in this paper, we investigate the tailoring of motivational mHealth features for the heterogeneous group of patients with CA. An exploratory study was carried out, consisting of semi-structured interviews and focus groups with 34 patients with CA and six health professionals. Through qualitative analysis, we first derive a typology distinguishing patients of the Defeatist, Worrier, Warrior and Cruiser type. Each type is characterized by their own disease management needs and coping strategies. Next, we unveil how each patient type has different preferred motivational strategies. These findings may inform healthcare informatics researchers who wish to tailor mHealth applications and interventions to the diversity of patients characterized by musculoskeletal diseases and chronic pain.
Designing infographics/visual icons of social network by referencing to the design concept of ancient Oracle Bone characters

This paper introduces the use of pictogram design concept in ancient China for the development of a set of today’s "graphic icons" or "infographics" in modern social network visualization systems. These graphic icons should be close to sensory symbols that derive their expressive power from their ability to use the perceptual processing power of the brain without learning. Therefore, with the use of such a set of "sensory symbols" we aim to achieve the identification of corresponding physical objects (or their attributes) to be performed close to the pre-attentive time.
Gamification and Predictive Analytics for the Next Generation of Workers

The implementation of psychological theories in the study of gamification has played an important role in our digital lives as multiple social media and gaming apps compete to harness user engagement to stay popular and relevant. A review of psychological theories that induce extrinsic and intrinsic motivations in the workplace, as well as the current applications of game elements in a non-gamified environment, is done in this report. Following that, a new gamified model is proposed that uses real-time data, Weibull statistical distribution, and a k-means clustering algorithm along with a reward system that would help increase skill development, employee satisfaction, and workforce retention.
3D Dual-Polarized Frequency Selective Rasorber Using Magnetic Material and Meander-Line

A 3D dual-polarized frequency selective rasorber (FSR) using magnetic material and meander-line is presented in this paper, which consist of the absorption and transmission channels. The wide absorption band is produced by the absorption channel consisting of the short-ended parallel-plate waveguide (PPW) loading the magnetic material and the meander-line, while the transmission band is produced by the open-ended lossless PPWs. The magnetic material is lossless in the transmission band resulting in the low-loss in transmission window, therefore the incoming EM waves cannot be absorbed by the absorption channel. An absorption band from 5.02 GHz to 9.60 GHz and a transmission band from 13.6 GHz to 16.4 GHz are obtained in the FSR presented, with the fractional bandwidth of 62.65% and 18.67% respectively. The thickness is 0. 18λa at 5.02 GHz. The experimental results shows that the presented FSR exhibits the advantages of dual-polarization and wide absorption. And no lumped components required reduced the fabrication cost greatly.