Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effect of Semi-Transparent and Interpenetrable Hands on Object Manipulation in Virtual Reality

Manipulating objects with the hand is a common task in Virtual Reality (VR). However, some issues can occur during these manipulations. Occlusion is one issue that happens when the virtual hand covers an object, and therefore the perception of that object is hindered. Semi-transparent hands could solve this problem. Another issue is the difficulty of delicate hand object manipulation. By using interpenetrable hands, turning off the physics of the hands, this difficulty might decrease. Still, there is a lack of research into how significant the impact is while using these methods. In this paper, with the designs of semi-transparent hands and interpenetrable hands, we present the results of our conducted user study focusing on the effect of semi-transparent and interpenetrable hands on hand object manipulation tasks in virtual reality. The user study includes a VR environment where participants are asked to perform tasks. These tasks are recorded in objective results of accuracy and speed. Afterwards, they fill out a questionnaire about their opinion on the difficulty while using the different methods in which the subjective findings are recorded. Additionally, we improved the semi-transparent hands by adding a feature that smoothly transitions the hand from opaque to semi-transparent. As an input device, we used the Leap Motion Controller (LMC) for this user study. However, any hand tracking sensor that tracks hands from the VR headset’s point of view could be used. Semi-transparent and interpenetrable hands have shown significant improvement for precise manipulation, which was verified by user feedback from the questionnaire and the data from the tasks.
Neural Networks for Cross-Section Segmentation in Raw Images of Log Ends

In this paper, wood cross-section (CS) segmentation of RGB images is treated. CS segmentation has already been studied for computed tomography images, but few study focuses on RGB images. CS segmentation in rough log ends is an important feature for the both assessment of wood quality and wood traceability. Indeed, it allows to extract other features like pith, eccentricity (distance between the pith and the geometric centre) or annual tree rings which are related to mechanical strength. In image processing, neural networks have been widely used to solve the problem of objects segmentation. In this paper, we propose to compare different state-of-the-art neural networks for CS segmentation task. In particular, we consider U-Net, Mask R-CNN, RefineNet and SegNet. We create an imageset which has been split into 6 subsets. Considered neural networks have been trained on each subset in order to compare their performance on different type of images. Results show different behaviors between neural networks. On the one hand, overall U-Net learns better on small dataset than the others. On the other hand, RefineNet learns well on huge dataset. While SegNet is less efficient and Mask R-CNN does not provide a detailed segmentation. This offers a preliminary result on neural network performances for CS segmentation.
RF SPST Switch Based on Innovative Heterogeneous GaN/SOI Integration Technique

This paper presents an innovative heterogeneous GaN/SOI integration technique implemented on an RF SPST switch. It is intended for 5G applications. The RF switch transistors are GaN based and the substrate is SOI. It allows to integrate RF, analog and digital in one monolithic circuit. The small and large signal measurements show respectively an insertion loss below 0.4 dB up to 30 GHz and a power handling higher than 38 dBm at 900 MHz.
Design of ionic liquids: an ecotoxicity (Vibrio fischeri) discrimination approach

Ionic liquids have attracted a lot of attention as potential replacements for conventional volatile organic solvents, although they may pose environmental risks to aquatic ecosystems that have to be assessed. There is strong interest in developing mathematical models to estimate the ecotoxicity of ionic liquids, minimising the experimental investigations and the consequent consumption of time and resources. This paper presents a new approach for estimating the ecotoxicity of ILs, based on the standardised assay with the bacterium Vibrio fischeri, by means of the application of Partial Least Squares-Discriminant Analysis (PLS-DA). The PLS-DA model developed makes it possible to discriminate ionic liquids, formed by combinations of 30 anions and 64 cations, on the basis of their expected toxicity with respect to conventional solvents that they may replace. The successful results obtained in the validation of the model reveal that this approach can be useful as a screening tool to easily aid, from the early stages of the process, the design of aquatic environmentally friendly ionic liquids. This approach may also be useful for the further development of predictive models based on other aquatic organisms, for which more data are expected to be available in the near future.
A Bayesian Optimization Framework for Analog Circuits Optimization

The growing complexity of analog circuits poses challenging constraints on analog simulation tools. Simulation based optimization approaches have gained a lot of interest to cut down the analog circuit design time and complexity. One of these approaches is the Bayesian optimization (BO) approach, which represents the analog circuit as a black box function, and incorporates optimization goal and constraints aiming to reach the optimum design parameters with the least possible simulation iterations. In this paper, a BO approach for automated sizing of analog circuits is discussed. The proposed approach uses Gaussian Process (GP) as a surrogate model and utilizes SOBOL sampling. The proposed algorithm is validated on a two-stage op amp benchmark circuit and compared to the literature work.
A Polarization-Insensitive Frequency Selective Surface Based Rasorber with Narrow-Band Absorption Between Two Transmission Bands

In this paper a Frequency Selective Surface (FSS) based rasorber has been proposed by combining the design of split-ring absorber and dual bandpass FSS on the front and back of a single 0.8 mm thick FR-4 substrate respectively. The proposed rasorber has two transmission windows at around 3.1 GHz and 8.4 GHz with insertion loss of 0.6 dB and 1.2 dB respectively. The transmission corresponds to the bandpass FSS and a resonant absorption at around 6.2 GHz corresponding to the split ring absorber. The parameters of the design are optimized such that at the absorption frequency the bandpass FSS acts as complete metallic ground. The proposed FSS based rasorber also verifies the polarization-insensitive behaviour due to its four-fold symmetry. A prototype of the proposed rasorber is fabricated and the results are experimentally verified.
Towards Effective Network Intrusion Detection: From Concept to Creation on Azure Cloud

Network Intrusion Detection is one of the most researched topics in the field of computer security. Hacktivists use sophisticated tools to launch numerous attacks that hamper the confidentiality, integrity and availability of computer resources. There is an incessant need to safeguard these resources to avoid further damage. In the proposed study, we have presented a meta-classification approach using decision jungle to perform both binary and multiclass classification. We have established the robustness of our approach by configuring an optimal set of hyper-parameters coupled with relevant feature subsets using a production-ready environment namely Azure machine learning. We have validated the efficiency of the proposed design using three contemporary datasets namely UNSW NB-15, CICIDS 2017, and CICDDOS 2019. We could achieve an accuracy of 99.8% pertaining to UNSW NB-15 whereas the accuracy in the case of CICIDS 2017 and CICDDOS 2019 datasets has been 98% and 97% respectively. A distinctive ability of the proposed model lies in its finesse to detect thirty-three modern attack types considerably well. Unlike conventional stacking ensembles, the proposed solution relies on a train-test ratio of 40:60 to establish the legitimacy of predictions. We also conducted statistical significance tests to compare the performance of classifiers involved in the study. To extend the functionalities further, we have automated the proposed model that can be a reliable candidate for real-time network intrusion detection.
Photocatalytic Effect of TiO2 Films on Viruses and Bacteria

This paper deals with photocatalytic decomposition of organic matter and antibacterial and antiviral effect of titanium dioxide (TiO2) thin films. The TiO2 films were deposited on glass substrates by plasma enhanced chemical vapor deposition (PECVD) in a vacuum reactor with radio frequency (RF) low temperature oxygen plasma discharge using titanium isopropoxide (TTIP) as a precursor. The depositions were performed with substrates preheated from 20 to 350 °C and with a systematical change of deposition conditions. The speed of photocatalytic decomposition of the model organic matter acid orange 7 (C16H11N2NaO4S), bacteria Escherichia coli and viruses HSV-1 (Herpes simplex virus) were evaluated and some clues on the destruction mechanism are discussed.
Application of gas chromatography-mass spectrometry in research of traditional Chinese medicine

It is well known that traditional Chinese medicine (TCM) plays a more and more important role in modern pharmaceutical industry. It has been used in the therapy of many diseases for several thousand years because of its high pharmacological activity, low toxicity and rare side effects. In TCM, as an important group of secondary metabolites, essential oils have attracted a great deal of attention in recent years. Gas chromatography-mass spectrometry (GC-MS) is the most commonly used technique for the analysis of liposoluble constituents, especially volatile/semi-volatile compounds, and their metabolites in biological fluids due to its high resolution, selectivity and sensitivity. This review briefly describes the applications of GC-MS for the isolation and characterization of volatile compounds from TCM. In addition, GC-MS methods adopted in the metabolic profiling of volatile compounds in biological matrices are also described.
Determination of pesticide residues in olive oils with protected geographical indication or designation of origin

Summary Seventy olive oil samples with protected geographical indication (PGI) or designation of origin (PDO) were analysed for fifty-one target pesticides by means of gas chromatography coupled to mass spectrometry. The highest detection rates were observed for penconazole (n = 20), α-endosulfan (n = 18), β-endosulfan (n = 16) and flufenoxuron (n = 12). Four of the seventy investigated samples contained pesticide residues in levels exceeding MRLs. However, the investigated samples showed decreased occurrence and levels of pesticides residues in comparison with previous studies concerning samples from Greek conventional and organic cultivations. According to Spearman matrix, powerful correlations were obtained between α-endosulfan and β-endosulfan, detected as pair in thirteen samples and between flufenoxuron and penconazole detected as pair in eleven samples. Among Greek olive oil samples, the Cretan ones showed the lower detection rates and the lowest average number of detected pesticides per sample (1.75) in the case of pesticides positive samples.
Complex-Valued Channel Attention and Application in Ego-Velocity Estimation With Automotive Radar

Attention mechanisms have been widely integrated with various neural networks to boost performance. However, when an attention mechanism was applied to a radar ego-velocity estimation network, the importance of carefully handling the amplitude and phase of complex-valued tensor was revealed. Therefore, in this study, we present a self-attention mechanism designed to handle complex-valued tensors in order to capture the rich contextual relationships implied within amplitude and phase. To exploit the advantages of complex-valued attention ( $\mathbb {C}\text{A}$ ), we evaluated its impact while performing ego-velocity estimation tasks based on radar data, whose amplitude and phase are related to the electromagnetic scattering of the target being observed. Radars are suitable sensors for such tasks as they are capable of long-range detection and instantaneous velocity measurement under variable weather and lighting conditions. In particular, we coupled our $\mathbb {C}\text{A}$ module with complex-valued neural networks, known to be particularly powerful for handling wave phenomena. The proposed method exhibits robust estimation performance, regardless of whether the Doppler ambiguity problem occurs and eliminates the dependence on preprocessing stages, including target detection and static target indication. Furthermore, it achieves improved stability during training via geometrical constraint regularization, and implicitly allows velocity conversion between the sensor and vehicle frames, even if the mount information of the sensor was not provided. Finally, ablation experiments conducted on extensive real-world datasets show noticeable improvement in estimation performance.
Existence of Twitter Users with Untraceable Retweet Paths and its Implications

Since only a few top tweets dominate the Twitter microblogging and social networking service (SNS), gaining a fuller understanding of so-called viral tweets is important for analyzing Twitter's complex and versatile nature. Therefore, in this study, we selected 24 viral tweets that received more than 3,000 retweets and investigated how such viral tweets spread through Twitter follower-followee connections. Then, through the construction of a retweet graph, we found that numerous retweeting users have untraceable retweet paths, which means they cannot be traced back to their root users. In particular, when the number of root user followers is relatively small (less than 10,000), we found that more than half of the retweeting users have untraceable retweet paths. One primary factor that can make users untraceable occurs when one of the user's previous retweeters has forwarded (retweeted) a viral tweet from the timeline of a public website where non-followers' tweets are visible. This consideration implies that a viral tweet posted by a user with a low number of followers can spread primarily via public Internet space rather than via personal follower-followee relationships. In contrast, viral tweets posted by users with numerous followers (more than 10,000) spread mostly through follower-followee connections and root users were found to be the most influential in their spread.
An Ensemble Learning Model for Agricultural Irrigation Prediction

In agriculture, many decisions rely on the experiences of farmers, and these decisions are often difficult to quantify with simple numerical values. However, manpower has gradually been replaced by machines in the progress of agriculture. The farmers change from the laborers to decision makers for expanding planting scale or improving quality. The agricultural Internet of Things (IoT) system has become the trend of new agriculture. The data monitoring and automation control system has helped many farmers. In addition, machine learning is also widely used in the agriculture. The irrigation is the most common. The main purpose of this thesis is to effectively use agricultural IoT systems and machine learning to improve traditional agriculture. This thesis provides an ensemble learning irrigation model based on the agricultural IoT system. The IoT system provides the data collection and data monitoring functions, and also supports the website and mobile applications for the convenience of farmers. The irrigation models are also embedded in the agricultural IoT system.
Distributed Adaptive Approach for Uncertain Nonlinear Multi-Agent Systems with Unknown Actuator Failures and Unknown Disturbances*

This paper investigates the distributed infinite-time control problem and distributed finite-time control problem for a class of uncertain nonlinear multi-agent systems with unknown actuator failures and unknown disturbances. By introducing distributed adaptive control technique, fuzzy logic systems and a bound estimation approach, two novel distributed fault-tolerant adaptive fuzzy control methods are proposed to ensure all followers well follow leader subject to unknown actuator failures and unknown disturbances. At last, both stability analysis and simulation results are provided to verify our control schemes.
Spherical Convolution Matched Layer in Cartesian Coordinate System for the FDTD Solver

Spherical truncation boundary in non-spherical coordinate system for the finite-difference time-domain (FDTD) method is discussed in this paper. The convolutional perfectly matched layer has been optimized in non-physical division and enhanced weakly ability in absorbing evanescent wave. From the radiation simulation, we found that the wave profile only according to simulation time, which leads to spherical outline in three dimensional cases. We reconstruct regular cubic Yee cells in three-dimensional, and restricts computation domain in the spherical truncation boundary. This method effectively avoids the unnecessary computation in corner and edge areas, and greatly reduces the memory and running time. This is an essentially efficient modeling strategy, which is suitable for the FDTD method in solving three dimensional scattering problems.
Application and preliminary evaluation of Anontool applied in the anomaly detection module

The goal of the work is to present a preliminary result of research about trade-off between privacy and utility in network traces. The study is considered in context of anomaly detection module with using neural network. The paper presents work in progress and the preliminary results of the evaluation of a anonymization algorithms of anomaly detection module. The anonymization algorithm has been briefly described and the results of the implemented neural network detection method using anonymized flow has been discussed.
Hybrid Beamforming Analysis Based on MIMO Channel Measurements at 28 GHz

Hybrid beamforming systems represent an efficient architectural solution to realize massive multiple-input multipleoutput (MIMO) communication systems in the centimeter wave (cmW) and millimeter wave (mmW) region. These hybrid beamforming systems separate the beamforming process into a digital and analog beamforming network. The analog beamforming networks can be realized by different architectural solutions, which demand dedicated algorithms to determine the complex weighting factors in the digital and analog domain. To date, novel hybrid beamforming architectures and algorithms are solely compared in numerical simulations based on statistical channel models. These abstract channel models simplify the complicated electromagnetic propagation process, thereby not exactly reconstructing the wireless channel. Within this work, we present a measurement-based evaluation of hybrid beamforming algorithms and compare them with numerical results gained from a statistical path-based MIMO channel model. The results show that by adjustment of the channel model parameter the simulation achieves a good match with the measured maximum achievable spectral efficiencies.
Adaptive Fuzzy Finite-time Tracking Control for A Class of DC Motor with State Constraints

This paper presents an adaptive fuzzy finite-time control scheme for a class of direct-current (DC) motor with state constraints. Firstly, the barrier Lyapunov function is used to constrain the state variables of the DC motor to ensure that the angular position and angular velocity of the DC motor are limited within the given constraint range. Secondly, the fuzzy logic system is introduced to approximate the unknown nonlinear function, and the finite-time control technology is utilized to improve the convergence speed and control accuracy of the system. Finally, the simulation results show that the proposed method can achieve fast and effective position tracking control for DC motor.
Optimization of PM-Assisted Synchronous Reluctance Motor with Asymmetric Rotor

The permanent magnet assisted synchronous reluctance motor (PMA-SynRM) with the asymmetric rotor design can effectively improve the utilization of magnetic torque and reluctance torque, thus to improve the torque density. However, it also brings about problems of cogging torque and excessive torque ripple. In order to solve this problem of excessive torque ripple, the torque ripple is suppressed from two aspects of the motor structure and control methods. In terms of motor structure, opening the auxiliary slot and changing the pole and slot combinations with different winding methods. In control methods, the torque ripple is suppressed by analyzing the torque harmonic components and injecting the specified harmonic current. Then, with the aid of finite element method (FEM), the influence of three methods on the torque characteristics and performance of the PMA-SynRM with asymmetric rotor structure is analyzed. The results show that the three methods can effectively suppress the torque ripple, and the 27-slot stator with distributed windings structure can effectively suppress the cogging torque and torque ripple of this kind of motor under the premise of ensuring the full use of the torque component. This work can better enhance the flux focusing characteristics of the asymmetric rotor structure, laying a foundation for the application of the asymmetric rotor PMA-SynRM in more fields.
Physicochemical Aspects of Deposition of Streptococcus thermophilus B to Hydrophobic and Hydrophilic Substrata in a Parallel Plate Flow Chamber

We measured the initial deposition rate onto, the blocked area per particle of, and the residence-time-dependent desorption of Streptococcus thermophilus B from fluoroethylene propylene (FEP), poly(methylmethacrylate) (PMMA), and glass, i.e., a series of increasingly hydrophilic surfaces, from a 40 mM potassium phosphate solution at pH 2 and 7. The initial deposition rates did not vary much with pH or collector surface hydrophobicity, presumably due to a high contribution of sedimentation to mass transport. However, even after elimination of sedimentation effects by averaging of top and bottom plate data, deposition efficiencies of over 90% were found. The effects of sedimentation were also obvious in the local distribution function g(x, y), showing a high degree of both upstream and downstream near-neighbor collection. The blocked areas ranged from about 10 particle cross-sections for all three collector surfaces at pH 7 to about 200 particle cross-sections for FEP at pH 2 and appeared governed by an interplay of electrostatic interactions and hydrophobicity, in a manner similar to the number of cells adhering in the stationary state. The initial and final desorption rate coefficients were lower at pH 7 than at pH 2, whereas the relaxation times at pH 7 were about two times larger than those at pH 2, indicating a faster transition from reversible to irreversible adhesion at pH 7 than at pH 2. Bond strengths were estimated from the desorption of adhering cells when exposed to a cell-free flowing suspension in order to eliminate the effects of collisions. Assuming a square potential well, we found bond strengths of 16-17 kT per cell, which corresponded remarkably well to estimates based on interfacial thermodynamics. In summary, the parallel plate flow chamber with real-time in situ image analysis, as used here, offers a wealth of information regarding the deposition process that might help to unravel the complex mechanisms governing microbial deposition onto solid surfaces. (C) 1994 Academic Press, Inc.