Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploiting the Computational Path Diversity with In-network Computing for MEC

With Computing in the Network technologies, Mobile Edge Computing (MEC) has expanded the resource distribution and tightly integrated computing-network capabilities from the end-devices, through the edge, to the cloud infrastructure, including at points in between. Thus, edge computing is able to deliver a more collaborative processing, better service responding to the increasing application needs in low latency processing. In the presence of integrated computing-network resources and their increased capacity, current proximity-to-data methods in edge computing lead to sub-optimal performance in terms of processing latency. Addressing this issue, this paper presents a Low-latency Adaptive Workload Allocation framework (LAWA) to harness the growing in-network computing resources to deliver low latency processing capabilities for emerging latency-constrained applications. LAWA defines an application by its computational source and destination. Considering the diversity of computing and network resources, we try to find an optimal computational path and its workload allocation. We model the problem as a mixed integer programming problem. To solve this problem, we propose the computational pathfinding and workload allocation algorithms with optimality guarantees. Experimental results show that, comparing with the state-of-the-art methods, our method achieves up to 8.04× speedup, in terms of end-to-end latency.
Down-Conversion Gilbert Cell Mixer Design for 5G Wireless Communications Systems

This paper presents a down-conversion Gilbert Cell mixer design operating in the 28 GHz millimeter-wave (mmWave) frequency band in 5G applications. The current bleeding technique is used to increase the conversion gain (CG) in the down-conversion mixer circuit, which leads to high linearity (IIP3) and low noise Figure (NF) performance. An intermediate frequency (IF) buffer has been coupled to the mixer outputs to improve linearity performance and facilitate impedance matching. The proposed mixer circuit is designed using a commercial 40 nm low-power CMOS technology. The mixer core and the IF buffer draw 43.61 mA from a 1.1 V supply voltage. According to post-layout simulation results, the mixer performs with a conversion gain of −2.32 dB, a noise Figure of 9.482 dB, and IIP3 of 12.667 dBm in the 28 GHz millimeter-Wave (mmWave) frequency band, including the current bleeding structure and the output buffers. Finally, high isolation between ports has been established, with LO-RF isolation below −50 dB, LO-IF isolation below −40 dB, and RF-IF isolation below −40 dB.
HARM: A Hint-Based Assertion Miner

This article presents HARM, a tool to generate linear temporal logic (LTL) assertions starting from a set of user-defined hints and the simulation traces of the design under verification (DUV). The tool is agnostic with respect to the design from which the trace was generated, thus the DUV source code is not necessary. The user-defined hints involve LTL templates, propositions, and ranking metrics that are exploited by the assertion miner to reduce the search space and improve the quality of the generated assertions. This way, the tool supports the work of the verification engineer by including his/her insights in the process of automatically generating assertions. The experimental results show real improvements with respect to the state-of-the-art in terms of assertion coverage and scalability.
Airplane Flight Phase Identification Using Maximum Posterior Probability Method

The flight of a civil airplane is supported by many different systems. However, during the flight pilots require different navigation and surveillance data to perform airplane guidance. Each flight phase requires the usage of different methods of guidance and maintaining a preplanned trajectory. Automatic flight phase identification is an important component of on-board data processing and in the ground surveillance data processing unit. In the paper we study plight phase identification using a probability-based approach. Flight phase is detected by vertical speed and current barometrical altitude by method of the maximum posterior probability. A normal probability density function is used as a conditional function due to the assumption of normal error distribution of barometrical sensor. The proposed approach has been verified with real traffic surveillance data obtained under automatic dependent surveillance-broadcast communication channel within Ukrainian airspace. Classification on four common flight phases of taxing, take-off, en-route, and landing is used. The proposed method guarantees flight phase recognition with a minimum risk of false detection and indicates well detection in case of gaps in data sets.
Identification of Safe Landing Areas with Semantic Segmentation and Contour Detection for Delivery UAV

Automatic identification of landing area helps UAV (Unmanned Aerial Vehicle) to land correctly and safely. UAV can use computer vision to detect special signs, such as QR code, April tag, and geometric shapes. However, such object detection lacks the ability to fully recognize its surrounding environment. Currently, advances in deep learning have the potential to replace these special markers and can recognize other objects around the landing area more accurately. Moreover, delivery UAV operating in urban areas are prone to uncertain conditions. Thus, this paper proposes a semantic segmentation method to segment objects in the delivery UAV landing area and identify safe landing area. Contour detection is used to extract the segmentation results to get the largest polygon in the contour and coordinate points of the landing target. The process of predicting each image takes 0.52 seconds. This overall method was successfully executed by using an i3 processor with an average processing speed of 4 frames per second. The results showed that the model has a good segmentation ability to identify the target of UAV landing area.
Intelligent Mineral Identification and Classification based on Vision Transformer

Image recognition technology in deep learning plays a vital role in many research fields. The intelligent recognition of mineral images based on deep learning technology brings new ideas for the development of traditional fields. It improves the efficiency of identification and brings some economic benefits. This paper proposes an intelligent mineral recognition classification model based on Vision Transformer. Firstly, More than 2000 images of twelve minerals, such as biotite, bornite, phoenix, and quartz, were collected. The data set was expanded by the data enhancement method, which was used to train and test the model, and the model's generalization ability was enhanced. Secondly, a self-attentive mechanism is introduced for feature extraction, and a new activation function is used to optimize the convergence speed of the model further. In the end, the accuracy of this model on the test set Top-1 reached 96.08 %, and the F1 score was 95.40 %. Compared with the network models such as ResNet50, VGG16, and DenseNet, the proposed model's recognition accuracy is higher, and the recognition stability is also better. According to the analysis of the experimental results, the pre-processing of the data also has a particular influence on the accuracy of the model, which provides an essential reference for the subsequent intelligent recognition and classification of minerals.
Mathematical Models of Chemical Reactions: Theory and Applications of Deterministic and Stochastic Models

A light projector for providing uniform surface area illumination of the type utilized in motion picture studios or the like, employs a housing having a reflector arranged at one end and a Fresnel lens arranged at the other end. At least two linear light sources are located in the interior of the projector at right angles to the optical axis and are electrically connected to a three-phase energization circuit. The lamps are arranged in a star-shaped pattern, relative one to another, and each is a single, self-operating, interchangeable unit, such that by replacing only one of the lamps in the light projector the color temperature of the illumination may be maintained approximately at the desired value.
GaN MMIC High Power Amplifiers for K-Band Satellite Payload

This contribution presents the activities carried out towards the realization of a high-power solid state power amplifier, based on Gallium Nitride (GaN) technology, targeting more than 125W of output power in the frequency range 17.320.2 GHz, conceived for the next generation K-band Very High Throughput Satellites (vHTS). For this purpose, specific Monolithic Microwave Integrated Circuits (MMICs) Power Amplifiers (PAs) were developed on a commercially available 100 nm gate length GaN on Silicon (GaN-Si) process (OMMIC process D01GH). The design was carried out considering space reliability constraints on electrical parameters and accounting for the spacecraft temperature limits, which are extremely challenging for this technology, to keep the junction temperature of all devices below $160^{\circ}\mathrm{C}$ in the worst-case condition (i.e., maximum environmental temperature of $85^{\circ}\mathrm{C})$. The final MMIC, based on a three-stage architecture, demonstrates on wafer and in pulsed condition to achieve a minimum output power and power added efficiency (PAE) of 10W (40dBm) and 35% (with a peak of 45%) in the full Ka-band satellite downlink, i.e., from 17.3 GHz to 20.2 GHz. The packaged version demonstrates in continuous wave (CW) conditions an output power larger than 39.5dBm with a PAE better than 30%. Moreover, long-term (24h) CW test at saturated output power has shown almost negligible performance degradation, thus providing confidence in the robustness of the selected GaN-Si technology.
Vacuum creation of massive vector bosons and its application to a conformal cosmological model

In the simple model of a massive vector field in flat space-time, we derive a kinetic equation of non-Markovian type, describing the vacuum pair creation under the action of external fields of different nature. We use for this aim the non-perturbative methods of kinetic theory in combination with a new element when the transition of the instantaneous quasiparticle representation is realized within the oscillator (holomorphic) representation. We study in detail the process of vacuum creation of vector bosons generated by a time-dependent boson mass in accordance with a conformal-invariant scalar-tensor gravitational theory and its cosmological application. It is indicated that the choice of the equation of state (EoS) of the Universe allows to obtain a number density of the vector bosons that is sufficient to explain the observed number density of photons in the cosmic microwave background radiation. It is shown that the vector boson gas created from the vacuum is in a strong non-equilibrium state and corresponds to a cold dust-like EoS.
Angle of Arrival Estimation in the Presence of Phase Difference Ambiguities

For a variety of applications, the task of global localization is to determine both, the position and the orientation of one or more mobile objects. An important area of application is mobile robotics, where knowledge of the heading angle is of great importance. One way to localize a mobile robot in real time is to use a network consisting of IEEE 802.15. 4a radio nodes based on Ultra-wideband (UWB) technology. In addition to measuring the signal propagation time, UWB radio nodes equipped with multiple transceiver modules can also estimate the Angle of Arrival (AoA) of the signal by capturing the phase differences. The additional angle information can be used to increase location accuracy and reduce the absolute number of radio nodes required in a deployment environment. For this reason, this paper investigates the feasibility of AoA estimation based on UWB radio nodes. Specifically, radio nodes from Nanotron Technologies/Inpixon based on the swarm bee ER module are employed. One problem of the specific circuit board design used is that the antenna spacing is so large that ambiguities occur when detecting the phase differences, which leads to large AoA estimation errors. Hence, methods for solving this problem are presented and their functionality is evaluated based on real world measurements.
Input Filter Design Issues for Capacitive-link Transformer-less EV Universal Battery Supercharger

A design methodology and optimization mechanism suited for differential mode (DM) capacitive-link transformer-less electric vehicle (EV) universal battery supercharger (UBS) is outlined. The unique design challenges related to the UBS are explored, including the effect of capacitive link and DM topology and modulation in the filter design. Moreover, the inherent structure of the Ćuk topology is used to reduce the number of passive components in an LCL input DM filter for this EV charger. Design constraints are defined in the context of the presented rectifier, and an algorithm to approach it is outlined.
Koopman-based data-driven control for large gantry cranes

This paper is concerned with control of large gantry cranes possessing flexible structural vibrations in the direction of trolley travel. Since these vibrations are mainly caused by the movement of the trolley itself, they can be actively damped by a redesign of the trolley motion control system. In this contribution, a data-driven control approach based on the Koopman operator is proposed and validated on simulations.
An Experimental Study for Load Matching in Wireless Power Transfer System using Class-E Inverter and Capacitor Array

It is critical to ensure load compatibility of Class-E inverters used in wireless power transmission systems. Two different methods can achieve load matching. The first is provided by adjusting the source voltage, and the second is by adjusting the capacitance in the resonant circuit. The second method is the most frequently used and practical in the literature. In the wireless power transmission system, the distance between the coils changes the inverter load value. The previously designed capacity array simulation study has been implemented in this study. In the application results, the load compatibility of the inverter has been tried to be ensured. As a result of the study, load compatibility of Class-E inverter is provided for variable distances.
Reduction of the Cost Needed for Converting a Conventional Building to a Nearly Zero Energy Building

This paper investigates the challenging problem of reducing the cost needed for converting a conventional building to a nearly-zero energy building (nZEB). This can be at-tained by properly selecting the sizing of the domestic renewable energy sources (DRES) and battery storage system (BSS), and improving the performance of the building electric microgrid. Thus, on the one side, a new methodology based on the genetic algorithm (GA) is proposed to properly determine the correct size of the DRES and BSS. On the other side, an integrated control method based on the GA technique too for the energy man-agement in the home microgrid is suggested that is accomplished through a correct balance between the maximum exploitation of the DRES and BSS, comfort of the building residents, and en-ergy saving. Therefore, the problem of reducing the cost for de-veloping an nZEB is addressed by reducing the two cost components, i.e. installation and operating cost. Moreover, the influ-ence of the one cost on the other is considered, and therefore an integrated calculation method is developed that provides a ho-listic solution for the nZEB's cost problem. The proposed calcu-lation strategy has been experimentally validated in a pilot building and several experimental results are presented in this paper to demonstrate the effectiveness, practicality, and functionality of the suggested methodology.
Vector Repetitive Control Based on Harmonic Sequence Extraction for Shunt Active Power Filters

Repetitive control (RC), which is widely applied in many fields, can track or eliminate periodic signals with almost zero steady-state error. However, the conventional RC (CRC) has a slow transient response so that it usually has to be used in combination with other fast control to adapt to high performance application’s request. In this paper, a vector repetitive control (VRC) for shunt active power filters (SAPF) is proposed. The VRC controller can speed up error convergence rate, and even make RC be not a slow control anymore by reforming the internal model of the CRC. Additionally, for a SAPF or other applications where harmonics need to be extracted, a harmonic sequence extraction based on trigonometric function delay signal cancellation (TFDSC-HSE) is proposed too. The proposed scheme (TFDSC-HSE+VRC) accelerates real-time tracking rate while maintaining outstanding steady-state response. A practical case study on a three-phase SAPF is conducted. The feasibility and effectiveness of the proposed scheme are verified by experiments.
Determination of Copper Leakage Configuration in Thermal Insulation of Induction Channel Furnaces After Years of Industrial Use

Peculiarities of interrelated electrophysical and thermal processes in industrial induction channel furnaces were studied, taking into account the leakage of liquid metal into their thermal insulation after many years of operation. On the example of UPCAST furnace type for copper melting in the Comsol Multiphysics software a mathematical simulation was carried out. The three-dimensional distribution of the electromagnetic and temperature fields in the volume and on the surface of the furnace elements with different configurations of the destruction of its thermal insulation was obtained. The relationship between the size and shape of the melt leakage and the temperature distribution on the furnace lining was established. The method of determining the damage configuration of furnace thermal insulation has been improved, which, unlike the previous ones, suggests using not only the maximum value of the surface temperature at the control points, but also the temperature gradient. Since the same maximum surface temperature on the furnace body can correspond to several variants of melt leaks into insulation with different ratios of dimensional parameters, the additional determination of the temperature gradient allows to clarify the variant of the configuration that is actually observed.
Electromagnetic Vibration Reduction of Permanent Magnet Synchronous Motor Using Particle Swarm Optimization Based on Response Surface

In recent years, there is a growing demand for permanent magnet synchronous motors (PMSMs) with reduced electromagnetic (EM) vibration, so as to achieve better NVH performance. EM vibration is induced by a set of spatial and temporal EM force harmonics, and can be evaluated through a 2-step finite element (FE) simulation. However, with a large scale of input parameters, it is impractical to optimize the EM vibration based on direct FE simulation due to the time consumption. In this paper, a response surface is generated based on the historical FE simulation data, replacing the FE simulation procedure. Based on the response surface, the EM vibration amplitude is optimized using the Particle Swarm Optimization algorithm. Since the optimization process no longer requires FE simulation, the optimization speed is greatly accelerated, and the optimal results can be obtained in a few seconds. As a result, the EM vibration of the motor is significantly reduced, while other performances (e.g. torque and efficiency) remain unchanged.
An 8 bit 1.73mW 1.25 GS/s Single-Core SAR ADC in 22 nm FDSOI CMOS

A low-power 8 bit single-core successive approximation register (SAR) analog-to-digital converter (ADC) with a sampling rate of up to 1.25 GS/s based on alternating comparators is studied. To prove the concept, the circuit is implemented in a 22 nm fully-depleted silicon-on-insulator (FDSOI) technology. Key power dissipating factors are explored considering the high sampling rate. A scheme is investigated, where each memory cell activates the second next memory cell to relax the speed requirement and allow for power savings compared to conventional approaches. A modified, non-overlapped signaling is used in the driver for the capacitive digital-to-analog converter (CDAC) to reduce a direct-path current and to ease the reference buffer design. Measurements show an effective number of bits (ENOB) of 6.86 bit near the Nyquist frequency. A power consumption of 1.73mW is measured, leading to the best Walden figure-of-merit of 11.9 fJ/c. -step for a single-core SAR ADC above 1 GS/s.
Microstrip GaAs Power Amplifiers for High Capacity 92–114 GHz 5G and 6G Backhaul

Single-ended and balanced 90–115 GHz microstrip amplifiers have been designed for cost-sensitive 5G and 6G backhaul in a new, commercial 6-inch, 0.1-μm GaAs process. At 108 GHz, measured output power is 20.4 and 22.5 dBm, respectively, which is among the highest reported for microstrip amplifiers at this frequency and competitive with expensive GaN processes. Good agreement is shown with simulation.
On the Complexity of Disaster-Aware Network Extension Problems

In this paper, we deal with the complexity of problems related to finding cost-efficient, disaster-aware cable routes. In particular, we compare two very different versions of the problem. In the first version, only the worst-case scenarios are considered, while in the second, the probability of the disasters is also known, and the aim is to find the most cost-efficient solution in terms of investment cost and risk of a network outage. Worstcase computations allow using efficient computational geometry algorithms, and even large networks can be analyzed. On the other hand, to find the cost-efficient soltuion, a lot of empirical hazard data must be processed, and heavy algorithms must be used. In this paper, we study the benefits and drawbacks of each model. In particular, we define multiple versions of the problem and investigate their complexity.
Non-Invasive Registration of Magnetic Particles in a Biological Medium Using Magnetic Field Sensors

Methods of noninvasive registration of magnetic particles and nanoparticles in a biological medium are considered. A simple analytical formula is obtained for calculating the maximum distance from the magnetic field sensor to the object under study, linking this distance with the parameters of magnetic particles and the threshold magnetic sensitivity δB of the sensor. Test magnetic measurements of aqueous suspensions containing superparamagnetic iron oxide particles with a concentration of 1-5 wt. % were carried out. The measured and estimated values of the maximum distance between the magnetized magnetic particles and the magnetic field sensor at which their registration is possible are compared. In the case when magnetic particles acquire magnetization in the Earth’s magnetic field, they are registered at a distance no greater than 23 mm from the magnetic field sensor with a strength of δB =10 nT. The estimate of this distance obtained according to the proposed formula is 8 mm. In another case, in a constant magnetic field of 70 mT, magnetic particles acquired a magnetization of 1.6-1.8 A⋅m2/kg, and the recorded and estimated distances (49 and 48 mm) practically coincide. It is shown that for some types of commercial magnetic particles having high values of magnetization (> 50 A⋅m2/kg), the specified distance can be < 200 mm, that is, they can be registered at any depth of their occurrence in the human body during the process of theranostics. At low concentrations ≤10−5 wt. % of magnetic particles, they can be controlled only by SQUID magnetometer having δB ≤10 fT.