Miracle-dz/UniFAR-FTUs huggingface dataset:
Two-phase active rectifier for two-phase permanent magnet Vernier generator

Recently the wind energy generation to supply low-power loads, such as traffic enforcement camera in highway is rising. For this purpose, a two-phase permanent magnet Vernier generator (PMVG) is considered which has some advantages like a low cogging torque. This generator is appropriate to use in the regions where the wind speed is low. An active rectifier for two-phase PMVG is introduced to receive power efficiently. In this paper, two different types of active rectifiers are addressed. The first one is the Vienna rectifier and the second is the boost active rectifier. A single-phase inverter is used in this structure to supply the load and then, the operation of these converters are simulated. Two Matlab/Simulink and Maxwell softwares are employed to simulate the converters and generator. Moreover, some features of these converters such as switch stress, number of elements, and control system complexity are compared. Experimental results verify the simulated results for the two-phase PMVG.
Cuttlefish: Neural Configuration Adaptation for Video Analysis in Live Augmented Reality

Instead of relying on remote clouds, today’s Augmented Reality (AR) applications usually send videos to nearby edge servers for analysis (such as objection detection) so as to optimize the user’s quality of experience (QoE), which is often determined by not only detection latency but also detection accuracy, playback fluency, etc. Therefore, many studies have been conducted to help adaptively choose best video configuration, e.g., resolution and frame per second (fps), based on network bandwidth to further improve QoE. However, we notice that the video content itself has significant impacts on the configuration selection, e.g., the videos with high-speed objects must be encoded with a high fps to meet the user’s fluency requirement. In this article, we aim to adaptively select configurations that match the time-varying network condition as well as the video content. We design Cuttlefish, a system that generates video configuration decisions using reinforcement learning (RL). Cuttlefish trains a neural network model that picks a configuration for the next encoding slot based on observations collected by AR devices. Cuttlefish does not rely on any pre-programmed models or specific assumptions on the environments. Instead, it learns to make configuration decisions solely through observations of the resulting performance of historical decisions. Cuttlefish automatically learns the adaptive configuration policy for diverse AR video streams and obtains a gratifying QoE. We compared Cuttlefish to several state-of-the-art bandwidth-based and velocity-based methods using trace-driven and real world experiments. The results show that Cuttlefish achieves a 18.4-25.8 percent higher QoE than the others.
An Experimental Gamma-ray Emission Computed Tomography System for Intensity Mapping of An Isotropic Sealed Source

At present, demands for measurement in industrial process control is advancing to another level, from one-dimension profiling to two- and three-dimension imaging technique. These advancements empowered and assisted in providing more information on the ongoing processes. For industrial process flow study, Single Photon Emission Computed Tomography (SPECT) system, can produce a cross-sectional image of the radioactive source distribution inside a vessel or pipeline. However, an optimum number of measurements is required to reconstruct the cross-section image without compromising or reducing the information on the reconstructed image. In this study, a time average emission computed tomography system was set up and utilised for mapping the intensity of the gamma-ray radiation from a static Barium, Ba-133 isotropic sealed source. The gamma-ray radiation source was placed on the centre and off-centre of the scanning region. Sodium Iodide, NaI scintillation detector then measure the intensity of the gamma-ray (emitted from the radioactive source) at a different angle. The data of gamma-ray intensity distribution were analysed, and the tomographic image of it was reconstructed using the Filtered Back Projection (FBP) algorithm. Although the SPECT system used is a time-average measurement, the tomographic image result is useful for the development of real-time industrial SPECT system that will be used for the imaging system in industrial process control.
Approximation of Fabry-Pérot resonances in Ag/quartz/Ag structures

We consider planar metal/dielectric/metal structures based on silver and quartz, which support excitation of the Fabry-Pérot modes in the case of close to normal light incidence. The resonance line shapes in the transmission spectra of the structures can be approximated by Fano line shapes. Based on analytical expressions for characteristics of resonances in lossy structures, the width, height, and slope of the Fabry-Pérot resonance line shapes are estimated. The characterization of Fabry-Pérot resonances may find potential applications in opto-informatics and sensing.
Adaptive Data Rate for Multiple Gateways LoRaWAN Networks

We propose to optimize the LoRaWAN Adaptive Data Rate algorithm in case an inter-packet error correction scheme is available. We adjust its parameters based on the analysis of the LoRa channel with multiple reception gateways, supported by real-world traffic traces. The resulting protocol provides very high reliability even over low quality channels, with comparable Time on Air and similar downlink usage as the currently deployed mechanism. Simulations corroborate the analysis, both over a synthetic random wireless link and over replayed real-world packet transmission traces.
A Multiple Linear Regression Model for Predicting Congestion in Heterogeneous Vehicular Networks

Finite capacity of network resources and enormous data generated by vehicles using safety and comfort applications, have made network congestion a challenge to manage in Heterogeneous Vehicular Network (HetVNET). In this paper, we propose a reliable network congestion model based on a Multiple Linear Regression (MLR), which is a supervised machine learning algorithm to predict network congestion in HetVNET. We have evaluated the performance of our proposed network congestion prediction model using a Cross-Validation test approach. Numerical results show that the proposed linear congestion prediction model is reliable, which can explain and support variability of the response as well. Moreover, we have weighted effectiveness of each considered HetVNET parameters, in association with congestion situation in HetVNET.
Features of a probabilistic model of intracardiac electrical activity during atrial fibrillation

Electrophysiological models of the heart, developed and studied to date, show the presence of many discrete events in time, leading to the propagation of electrical activity signals and allowing synchronizing (normally) the work of all parts of the heart. When considering models of heart rhythm management, at least two main levels can be distinguished: intra- and extracardiac. Each of these levels can also be divided into several sublevels. Intracardiac effects that regulate heart rate include the myogenic, intercellular, intracardiac nervous system and the effects of intracardiac humoral factors (produced in the heart itself). Extracardiac effects include neuroreflex and general humoral regulation of cardiac activity. Probabilistic models of the processes of cardiac activity are mainly used to describe heart rate variability at an extracardiac level. At the same time, probabilistic models were practically not used for the processes underlying the automatism of the heart at the intracardiac level. A probabilistic model of heart rate dynamics is proposed based on the concept of heart contraction because of the influence of many hierarchical random events. The results of the probabilistic modeling presented in the work are in good agreement with the well-known bioelectric models that describe the dynamics of intracardiac activity and make it possible to use a sequence of RR durations (for example, a rhythmogram and a pulsogram) to detect various latent cardiac abnormalities, in particular, atrial fibrillation.
Comparing and Adapting Propagation Models for LoRa Networks

In a wireless network, understanding the spatiotemporal propagation of a radio signal and its attenuation over distance has always been a great concern. However, to set up an efficient network with these needs, it is imperative to have a good characterization of the signal propagation over the deployment environment. The contribution of this paper is twofold. First, we study, select and test some of existing signal propagation models on a LoRa network in order to see which model best fits LoRa signal propagation behavior. Second, we empirically optimize the best model from the first phase. The resulting model is then tested and validated in another real-world environment and compared to other models already experimented for LoRa networks. The Hata model is found in the first phase to show more accurate results and is therefore adapted using real measured data. The adapted model from Hata is then tested and validated with another and larger data set. Comparisons with Lee and Oulu models that have been used in previous real LoRa networks studies show that our adapted model can provide more accurate predictions to assist LoRa network deployment campaigns.
Aircraft Trajectories Correction using Sharing Operative Meteorological Radar Information

Trajectory prediction and optimization capabilities are considered as crucial part for efficient Air Traffic Management (ATM) operation. One of the key factors that influence onto trajectory prediction is weather situation at the departure and arrival points and along the flight route. In this context it is crucial to utilize widely systems of operative obtaining information about weather hazards for short-term flight trajectory correction. Onboard meteorological radars are powerful and convenient tool for operative data obtaining during the aircraft flight when atmospheric and weather disturbances arise. In this paper possibilities of trajectory correction by providing accurate and operative meteorological data using the onboard radar system are shown and discussed.
Resonant nanophotonic structures for analog optical computing

Photonics is nowadays considered as one of the scientific fields promising for the further development of computing systems. In this review, we discuss recent advances in the research and development of nanophotonic structures for analog optical computing. We will focus on the optical implementation of basic operations of temporal and spatial differentiation and integration of the optical signals.
Behavioral Modeling of GaN Doherty Power Amplifiers Using Memoryless Polar Domain Functions and Deep Neural Networks

In this paper, novel Doherty Power Amplifier (DPA) models are presented. The motivation behind the proposed models is to accurately predict static nonlinearities in the compression regions of the carrier and peaking amplifiers. DPAs suffer from a nonlinearity that originates from the carrier amplifier, and a second more pronounced nonlinearity generated at the full compression region following the turn-on of the peaking amplifier. Moreover, these distortions are often observed at different input power levels depending on whether the AM-AM or the AM-PM characteristic is considered. Therefore, the proposed static model is based on independent modeling of the memoryless gain in the polar domain. The static model of the memoryless AM-AM and AM-PM characteristics is augmented with either memory polynomials or deep neural network functions for memory effects modeling. The methodology of building the proposed models and the achieved results are discussed in this paper. The MP based proposed model achieves an NMSE as low as −45.3dB with only 78 model parameters, while the DNN based model achieves an NMSE as low as −46.1dB with only 156 model parameters. However, the DNN based model achieves the best model resilience to changes in the identification data.
Demo: Skip Graph Middleware Implementation

Skip Graphs are Distributed Hash Table (DHT)based data structures that are immensely utilized as routing overlays in Peer-to-Peer (P2P) applications. In this demo paper, we present the software architecture of our open-source implementation of Skip Graph middleware in Java. We also present a demo scenario on configuration and constructing an overlay of Skip Graph processes in a fully decentralized manner. Our implementation is capable of hosting data objects at the Skip Graph processes and serving as a P2P data storage platform as well. Our middleware implementation provides an open-source platform to support Skip Graph-based applications on top of it.
Mobile Malware Detection with Imbalanced Data using a Novel Synthetic Oversampling Strategy and Deep Learning

Mobile malware detection is inherently an imbalanced data problem since the number of benign applications in the market is far greater than the number of malicious applications. Existing methods to handle imbalanced data, such as synthetic minority over-sampling, do not translate well into this domain since mobile malware detection generally deals with binary features and these methods are designed for continuous features. Also, methods adapted for categorical features cannot be applied here since random modifications of features can result in invalid sample generation. In this work, we propose a novel technique for generating synthetic samples for mobile malware detection with imbalanced data. Our proposed method adds new data points in the sample space by generating synthetic malware samples which also preserves the original functionality of the malicious apps. Experiments show that the proposed approach outperforms existing techniques in terms of precision, recall, F1score, and AUC. This study will be useful in building deep neural network-based systems to handle imbalanced data for mobile malware detection.
A 94 GHz FMCW Radar Easy Scaled to Focal-Plane Array Imaging System

A 94 GHz frequency-modulated continuous-wave radar that is regarded as a base for creating a focal-plane array radar imaging system has been developed. The key component of the radar is an original receiver module of a simple design and with low local oscillator power requirements. The radar prototype is described and demonstration of its operation capability is presented. The problems of developing a focal-plane array radar are discussed.
On the Coefficients of the Ehrhart Tensor Polynomials

The term of Ehrhart tensor polynomials is a natural popularization of the Ehrhart polynomial of lattice polytopes which is a much more used in different applications. As our fundamental instrument, we present $\mathrm{h}^{\mathrm{r}}$-tensor polynomials which expand the idea of the Ehrhart $\mathrm{h}*$-polynomial for a polytope, and networks. It also examines their coefficients for positive semi definiteness rather than the standard $\mathrm{h}*-$ polynomial. This paper gives the proof of two theorems that compute the coefficients of the Ehrhart Tensor polynomials (h3 and $\mathrm{h}^{4}$). The positivity for these coefficients is also proved. Some applications on the Ehrhart tensor polynomials are given.
Fork-Coupled Resonators for Characterization of Mold Material for 5G Applications

The dielectric properties of materials applied in advanced packaging technologies such as Fan-out Wafer Level Packaging (FoWLP) must be precisely known at millimeter-wave (mm-Wave) frequencies for optimized package design. In this paper, the applicability of fork-coupled resonators for the electromagnetic characterization of an epoxy molding compound (EMC) for 5G applications is investigated. The analysis is experimentally verified by means of measurements of fabricated test structures on EMC (Nagase R4601-X65) material for 5G applications in frequency bands of 26.5-29.5 GHz and 37-40 GHz.
On the Development of a Tool to Detect Pinocchio Effect Using Facial Heat Distribution

Thermography combined with experimental psychology and image processing has great potential in many applications including marketing, social media, and security. One of the challenges of adopting this tool is the large amount of data needed to get accurate results. Currently, an experimental psychologist manually detects and classifies each thermograph, resulting in time consumption and inefficiency that prevents large data analysis, leading to unreliable results. The Pinocchio effect refers to changes in nasal temperature when someone lies. This is a result of changes of blood flow in the area. This work develops a tool that helps detect and track this effect. The tool allows for large data to be collected and analyzed.
Transient Performance Limits for Ultra-Reliable Low-Latency Communications Over Fading Channels

In this paper, we develop the transient performance limits of wireless transmissions for ultra-reliable low-latency communications (URLLC). In particular, we derive the exact statistics of the maximum amount of data that can be delivered during a fixed time duration over fading channels, for both channel state information (CSI) at transmitter side and CSI at receiver side only scenarios. Following a recently proposed data-oriented approach, we then analyze the performance of sample ideal transmission schemes in terms of URLLC support. Selected numerical examples are presented to examine the effect of power adaptation and rateless coding on transient transmission performance over fading channels.
Chitinolytic Bacillus subtilis Ege-B-1.19 as a biocontrol agent against mycotoxigenic and phytopathogenic fungi

Abstract Background Biological control of pathogenic fungi is a possible alternate to the chemical control, which is harmful to humans and environment. Soil-borne Bacillus strains can be potential biocontrol agents and a source of lytic enzymes. Aim This study aimed to examine biocontrol potential and lytic enzyme activities of a soil isolate Bacillus subtilis Ege-B-1.19. Materials and methods Strain was identified by biochemical and 16S rRNA gene analysis and its biocontrol activity was investigated against Aspergillus niger EGE-K-213, Aspergillus foetidus EGE-K-211, Aspergillus ochraceus EGE-K-217, Fusarium solani KCTC6328, Rhizoctonia solani KACC40111 and Colletotrichum gloeosporioides KACC40689. Chitinase, chitosanase, N-acetyl-β-hexosaminidase and protease activities of B. subtilis Ege-B-1.19 were also determined. Chitosanase was purified using Sephadex G-150 column and its molecular weight was determined by SDS-PAGE. Chitooligosaccharides production using chitosanase was carried out and analysed by TLC and HPLC. Results Results depicted that B. subtilis Ege-B-1.19 has shown inhibitory effects against all the test fungi. Chitinase, chitosanase, N-acetyl-β-hexosaminidase and protease activities were determined as 2.7 U mL−1, 7.2 U mL−1, 6.2 U mL−1 and 12.2 U mL−1, respectively. Molecular weight of purified chitosanase was 44 kDa. Chitosanase hydrolysed chitosan to glucosamine (GlcN), dimers (GlcN)2 and trimers (GlcN)3. Conclusion Bacillus subtilis Ege-B-1.19 can be anticipated as useful biocontrol agent and its chitosanase can be utilized for enzymatic synthesis of chitooligosaccharides.
Data visualization and clustering in the task of system analysis of the patients examination results in the initial stages of cognitive impairment

There are several numbers of diseases and conditions which contribute the progress of cognitive impairment and deterioration of cognitive functions compared to an individual norm. Currently, the causes and progression of cognitive impairment are poorly researched. This paper presents a system analysis of data including 152 patients and the results of their examination. Various machine learning models were used in the research, including classification and clustering algorithms. Methods of data preprocessing and dimension reduction were used for visualization. The result models have demonstrated the possibility of applying the latest artificial intelligence approaches for data analysis in the medical field. It was possible to achieve an accuracy of the cognitive impairment diagnosis in the initial stages of 75% despite the small data set.
A Novel Deep Learning Scheme for Motor Imagery EEG Decoding Based on Spatial Representation Fusion

Motor imagery electroencephalography (MI-EEG), which is an important subfield of active brain–computer interface (BCI) systems, can be applied to help disabled people to consciously and directly control prosthesis or external devices, aiding them in certain daily activities. However, the low signal-to-noise ratio and spatial resolution make MI-EEG decoding a challenging task. Recently, some deep neural approaches have shown good improvements over state-of-the-art BCI methods. In this study, an end-to-end scheme that includes a multi-layer convolution neural network is constructed for an accurate spatial representation of multi-channel grouped MI-EEG signals, which is employed to extract the useful information present in a multi-channel MI signal. Then the invariant spatial representations are captured from across-subjects training for enhancing the generalization capability through a stacked sparse autoencoder framework, which is inspired by representative deep learning models. Furthermore, a quantitative experimental analysis is conducted on our private dataset and on a public BCI competition dataset. The results show the effectiveness and significance of the proposed methodology.
The phase diagram of the Ni-H system revisited

The phase diagram (the composition-temperature diagram) of the Ni-H system reported in our previous paper has been revised by adopting a more appropriate value for the H-induced volume, 2.2 A 3 /H-atom. Some additional data on the reduction of the melting point due to dissolution of hydrogen are also presented.
Effective dielectric functions of the tungsten and tungsten oxide particles under high power laser pumping

A method for recovering the laser-pump-dependent effective dielectric functions of Rayleigh and Mie particles is discussed. Values of the pump-dependent intensity of non-linear scattering and non-linear extinction of diluted suspensions of particles are applied as input parameters in the recovery procedure. These values are obtained using a modified z-scan technique with simultaneous measurements of the intensity of light scattered at the right angle to the direction of the probe laser beam. Two modifications of the recovery procedure, which are applicable in cases of small-sized (Rayleigh) and large-sized (Mie) particles are considered. The recovery techniques were verified using experimental data and simulation results for suspensions of tungsten and tungsten oxide particles with various sizes.
Psycho-Emotional State Analysis System of APCS Operator

The human factor plays a significant role in ensuring the integrated safety of technological facilities. It is important to monitor the state of the operator of automated process control systems in soft real-time mode in order to reduce the risk of attention and concentration losses. Paper discusses the issues of increasing the efficiency of monitoring system of the operator’s state by using algorithms for assessing the psycho-emotional state. These algorithms apply methods of intelligent analysis of video sequence data without the use of additional contact sensors, which reduces probability of making a wrong decision due to the timely detection of unstable psycho-emotional states. The accuracy of detecting unstable psycho-emotional states on a test data set is 79%.