Miracle-dz/UniFAR-FTUs huggingface dataset:
Elements of On-Board Diagnostics of Vehicles with Various Automation Levels by Iterative Forecasting Method

The article discusses the problem of preventing technical failures that can lead to an emergency when solving a tactical control task or a complete failure of the strategic task of driving a vehicle. Various types of diagnostics of the technical condition of a car are considered, in particular, at service stations, including visual inspection, the use of specialized stands or diagnostic complexes, as well as on-board tools, including direct and indirect. The author of the article proposes to use the method of iterative forecasting developed by him as a new type of on-board diagnostics. A mathematical model is presented for calculating the residual life of various vehicle units and their elements, such as brake discs, clutch, electric fuel pump, generator, constant-velocity joint boot, elastic suspension elements.
A 12-bit 20MS/s Asynchronous SAR ADC

A 12bit 20MS/s asynchronous successive approximation register (SAR) analog-to-digital converter (ADC) is presented in this paper. Segmented DAC technology and merged capacitor switching (MCS) are applied to reduce chip area and power dissipation. A floating inverter amplifier (FIA) assisted comparator is used to reduce noise and power consumption. The ADC is designed and simulated in a 180nm CMOS process. The simulation results show that the ADC consumes 1.3mW, achieving 70.48dB SNDR at 20MS/s, resulting in an SNDR-based Schreier figure of merit (FoM) of 169.3dB.
Knowledge Distillation for Mobile Quantitative Ultrasound Imaging

In this paper, we present a mobile-friendly quantitative ultrasound imaging network (MQI-net) that can provide high-quality quantitative ultrasonic images in resource-limited portable environment. Quantitative ultrasound imaging has been studied to identify biomechanical properties of tissue on an absolute scale and it now demonstrates clinical potential in differential cancer diagnosis and hepatic steatosis. Recently, learning-based approaches demonstrated the real-time reconstruction of quantitative ultrasound images while using conventional ultrasound systems. However, such methods require extensive computations which hinders rapid real-world adoption. To overcome the limitation, the MQI-net is proposed. It reduces the neural network parameters by 96% compared to the conventional convolutional neural network. In addition, a knowledge distillation scheme is employed in MQI-net to enhance reconstruction accuracy. The performance of the system is assessed through numerical simulations and in-vivo breast patient measurements. The proposed knowledge distillation scheme enhances the accuracy of the MQI-net by 33% and 17% for attenuation coefficient and speed of sound reconstruction, respectively
A SVM Transformer Fault Diagnosis Method Based on Improved BP Neural Network and Multi-parameter Optimization

SVM multi-class expansion strategy based on BP neural network is used for transformer fault diagnosis, which has higher classification accuracy than traditional multi-class SVM. However, this method needs to train the initial weight threshold to diagnose transformer faults, and its coding calculation process is complicated. To solve this problem, firstly, a new fault diagnosis method for SVM transformer based on improved BP neural network, is proposed in this paper, which embeds the guiding module and increases the weight of the feature vector extracted by the network, so as to improve the diagnosis and classification accuracy of SVM greatly. Then, based on the improved BP-SVM algorithm, a multi-parameter optimization of SVM kernel function and penalty factor based on K-fold cross validation and artificial bee colony algorithm is proposed. By selecting the optimal kernel function parameters, so as to optimize the comprehensive diagnosis method of transformer faults, greatly improve the diagnosis efficiency. Finally, it provides technical support for the inspection of transformer equipment.
Small Sample Bias in GMM Estimation of Covariance Structures

We examine the small sample properties of the GMM estimator for models of covariance structures, where the technique is often referred to as the optimal minimum distance (OMD) estimator. We present a variety of Monte Carlo experiments based on simulated data and on the data used by Abowd and Card (1987, 1990) in an examination of the covariance structure of hours and earnings changes. Our main finding is that OMD is seriously biased in small samples for many distributions and in relatively large samples for poorly behaved distributions. The bias is almost always downward in absolute value. It arises because sampling errors in the second moments are correlated with sampling errors in the weighting matrix used by OMD. Furthermore, OMD usually has a larger root mean square error and median absolute error than equally weighted minimum distance (EWMD). We also propose and investigate an alternative estimator, which we call independently weighted optimal minimum distance (IWOMD). IWOMD is a split sample estimator using separate groups of observations to estimate the moments and the weights. IWOMD has identical large sample properties to the OMD estimator but is unbiased regardless of sample size. However, the Monte Carlo evidence indicates that IWOMD is usually dominated by EWMD.
The system based on GRU and BiFPN to health monitoring of belt conveyor

As one of the main equipment of the terminal, the belt conveyor will have various faults in the complex working environment of the bulk cargo terminal. This paper mainly introduces a remote health monitoring method based on gated recurrent unit (GRU) network combined with mobile edge computing technology. The composition of belt conveyor is complex, and the amount of information generated by the equipment after the deployment of corresponding sensors is very large. Therefore, when using a single method based on the Internet of things to monitor its health, the huge amount of data generated needs to be analyzed, stored and transmitted, which cannot meet the conditions of real-time monitoring. These data can be processed using 5G mobile edge computing technology combined with deep learning methods. In the method mentioned in this paper, acquired spectrum coefficient (MFCC) and weighted bidirectional Feature Pyramid network (BiFPN) are used to preprocess the collected data, and then the data is transmitted to the GRU network, so that the system can learn the relevant fault state information, and finally the model can judge the fault information. Mobile Edge Computing (MEC) is the deployment of nodes between workers and the cloud and close to the data. With the help of data preprocessing methods and GRU networks, the analysis, judgment and transmission are carried out in the 5G environment. This method can monitor and judge the fault of belt conveyor in real time and efficiently, and realize the unmanned health monitoring of belt conveyor.
A General Equivalent Circuit Model for PMUTs Array Working in Multi-Vibration Modes

This paper presents a general equivalent circuit model for piezoelectric micromachined ultrasonic transducers (PMUTs) array, with the consideration of the multi-mode vibration of the PMUTs' diaphragm, for the first time. And the results derived by the model achieves a good agreement with the Finite-element-method (FEM) simulation and experimental results, which lays a solid foundation for designing and optimization of the PMUTs working in broad frequency range.
Taxi travel time based Geographically Weighted Regression Model (GWR) for modeling public housing prices in Singapore

In this research, a taxi travel time based Geographically Weighted Regression model (GWR) is proposed and utilized to model the public housing price in the case study of Singapore. In addition, a comparison between the proposed taxi data driven GWR and other models, such as ordinary least squares model (OLS), GWR model based on Euclidean distance and GWR model based on public transport travel time, have also been carried out. Results indicates that taxi travel time based GWR model has better fitting performance than the OLS model, and slightly better than the Euclidean distance-based GWR model, however, it is not as good as the GWR model based on public transport travel time according to the metric of Adjusted R2. These experiments indicate that the public transport travel time may has a major part to play in modeling the public housing resale prices compared to taxi travel time or driving time, and both the taxi travel time and public transport travel time can better explain the public housing resale prices in Singapore compared to Euclidean distance in the GWR modeling.
Simulation Method for Energy Estimation of Variable Speed Pump for Heating System

Model based energy management for a heating system driven by the circulating centrifugal pump with variable speed drive in household applications is a promising approach for potential energy savings. The paper presents a developed Matlab/Simulink model of an energy savings calculation for a dwelling supplied with a circulating heating system (HS). The heating system includes a circulating centrifugal pump that has a variable speed drive helping to carry out the control algorithm based on the energy management strategy. The model helps to select the proper control strategy to achieve the desired indoor climate by following the defined reference point to keep the indoor temperature within the desired range. It consists of several main parts such as parameters selection module, thermal module, and integrated circulating centrifugal pump. The proposed simulation model helps to determine the heating power that is necessary to satisfy thermal requirements for a residential building by taking into account the thermal criteria with minimal energy consumption. The model helps to evaluate the energy savings of the heating system in the broad range of outdoor temperature variations.
6dF: a very efficient multiobject spectroscopy system for the UK Schmidt Telescope

Multi-object spectroscopy at the Anglo-Australian Observatory's 1.2-m UK Schmidt Telescope (UKST) is carried out with the FLAIR multi-fiber system. The FLAIR front-end feeds an optically-efficient, all-Schmidt spectrograph mounted on the dome floor. However, positioning of the 92 available fibers within the 40 sq. deg. field of the telescope is essentially a manual operation, and can take from four to six hours. Typical observations of sufficient signal-to-noise usually take much less than this (e.g. about an hour for galaxy redshifts to B approximately 17). Clearly, therefore, the system is working at well under its potential efficiency for survey-type observations where repeated reconfigurations of fibers are required. To address the imbalance between reconfiguration time and observing time, a fully-automated, off telescope, pick-place fiber-positioning system known as 6 dF has been proposed. It will allow 150 fibers to be reconfigured across a 6-degree circular field in under an hour. Three field plates will be available with a 10 - 15 minute field-plate changeover anticipated. The resulting factor of 10 improvement in observing efficiency will deliver, for the first time, an effective means of tackling major, full-hemisphere, spectroscopic surveys. An all southern sky near-infrared-selected galaxy redshift survey is one high- priority example. The estimated cost of 6 dF is $A450k. A design study has been completed and substantial funding is already in place to build the instrument over a two-year timescale.
The Extension of the Cascade Algorithm to Large Graphs

Large graphs for which the calculation of the shortest distances between all vertices is required often consist of several parts with limited interconnections. This paper shows how this characteristic may be used to reduce computer storage for a matrix type calculation, to reduce calculations] labour, and to provide results in a form which is particularly convenient if a subsequent assignment of commodity flow to shortest paths is needed. The technique presented here allows the partition to be arbitrary, save for a single, easily achieved, condition.
Parity-Time Symmetric MEMS

Parity (P)-Time (T) symmetry is the concept of quantum mechanics and has been extended to classical systems because of the mathematical equivalence between the Schrödinger equation and the classical wave equation. PT-symmetric systems are invariant under the joint parity and time reversal operation. They have two distinguished regimes: an exact PT-symmetric regime with real eigenvalues and a broken PT-symmetric regime with complex-conjugate eigenvalues. An exceptional point (EP) at which the eigenvalues and corresponding eigenvectors simultaneously coalesce separates the exact regime from the broken regime. Systems at the EPs exhibit a strong response to a small perturbation. Therefore, EPs-based sensors have recently received significant attention in optics and photonics, acoustics, and electronics. Our group has applied PT-symmetry to MEMS (Microelectromchanical Systems) design, leading to enhanced sensing of micromachined silicon resonators and piezoelectric resonators, respectively. This article presents the preliminary results of PT-symmetric MEMS.
Core Loss Calculation and Temperature Distribution Simulation of Saturable Reactor for Converter Valves

Saturable reactor is a key equipment of HVDC transmission, and its insulation failure will severely affect the safe operation of the converter valve. For the cause of studying the loss and temperature distribution of the core-type saturable reactor, a simulation model of the saturable reactor is established using COMSOL Multiphysics software. Taking the instantaneous current of 3000 A under the full load as the excitation source, the loss distribution of the core is obtained. Afterwards, the loss density distribution is taken as the heat source of the temperature field. The electromagnetic thermal simulation model of the saturable reactor is built to analyze its temperature distribution. The results show that the loss of the core inside is higher, and the loss at the upper and lower ends is lower than that in the middle. It is found that the highest temperature of the core is 62.2 °C. The inner temperature of the core is higher than that of the outer part. The results can provide a reference for the heat dissipation improvement of the core-type saturable reactor.
Segmentation of 2D cardiac ultrasound with deep learning: simpler models for a simple task

Low-complexity convolutional neural networks have been shown to be sufficient for segmentation of cardiac US images in A2C and A4C views. The performance of 24 varying-complexity implementations of U-Net and DeepLabV3+ (popular segmentation architectures) has been tested on cardiac US data (CAMUS data set) and street view data (Cityscapes data set). The inference speed of the models has also been measured before and after post-training optimization. The models systematically differed in their structural components: the number of layers and convolutional filters as well as the receptive field size. All models trained to maximize the Dice Coefficient. The Dice Coefficient was consistently high (0.86-0.90) on CA-MUS data and low (0.48-0.67) on Cityscapes data for all models. Each ten-fold reduction in the number of model parameters tended to reduce the score by ≈0.01 on CAMUS and by 0.03-0.05 on Cityscapes. Likewise, low-parameter models, especially the ones based on U-Net, had yielded predictions with higher (worse) Hausdorff Distance values. Increasing the receptive field size of the models partially mitigated this effect. Without post-training optimization, the inference speed mostly varied with the number of layers in the networks. The least complex U-Net model was 83% faster than the most complex one; for the DeepLab models the difference was 53%. With post-training optimization, any reduction in the number of parameters led to increased speed: up to more than 700% for both architecture types.
Techno-Economic Optimization of a Spoke-Type Ferrite Magnet Generator for Hydropower Plant Based on Metamodeling

This paper presents the techno-economic metamodel-based optimization for different slot-pole combinations of a spoke-type interior permanent magnet synchronous generator. Optimization procedure and strategy were implemented in Ansys OptiSlang and Ansys Motor-CAD using MATLAB script. The novelty of the approach is reflected in the automatic three-line assessment of the demagnetization proximity implemented into the optimization procedure. Three slot-pole combinations, namely 135s30p, 144s24p i 144s30p, were optimized and compared based on generator active material cost and efficiency with the given optimization constraints.
A Receiver Only Time Synchronization Protocol for UAS. Simulation Results

This paper presents a numerical analysis of a Receiver Only Synchronization (ROS) protocol for use by Unmanned Aircraft Systems (UAS). We obtained clock offset and clock skew estimations using the number of iterations and number of observations as independent parameters. The delays were assumed to be autocorrelated. Two delay distributions, i.e., zero-mean gaussian and exponential, were considered. We checked dispersion of results in a zero-mean gaussian case against the Cramér-Rao Bound (CRB) limit. Results confirmed that the scheme proposed was fully efficient. However, clock offset estimations in the exponential case was less accurate than with the Gaussian, zero-mean distribution. In the second part, we imposed a wake/sleep scheme on the model, concluding that for ratios below 92/08 results were not accurate at p=. 05 level. The tests were performed in Matlab®. Based on the results, a venue confirming the assumptions made was outlined for future work.
Design of Variable-Load Class-E Inverter Using Laplace Based Steady-State Modeling

Conventional class-E inverters that utilize large “choke” input inductors can only achieve zero-voltage-switching (ZVS) over a small range of load resistances. However, class-E inverters with finite input inductance can maintain ZVS over a wide range of loads. This paper introduces a design methodology for finite-input-inductance class-E inverters that achieve ZVS over a wide load range while having minimum circulating current. This methodology is based on an accurate analytical steady-state model of class-E inverters, which is derived using a recently proposed approach called Laplace Based Steady-State Modeling (LBSM). Unlike conventional analytical models, the LBSM-based accurate model enables precise selection of the input inductance for the class-E inverter. A 1.32MHz class-E inverter prototype designed using the proposed methodology is built and shown to achieve ZVS over a wide range of load resistances.
A V-band Power Amplifier for Satellite Communications in 40-nm CMOS

This paper presents a V-band power amplifier (PA) with two stages. Each stage consists of a neutralized common-source amplifier pair, coupled with each other using a transformer. Implemented in 40-nm CMOS technology, the proposed two-stage PA achieves a measured small-signal gain of 16.8 dB at 47 GHz with a 3-dB bandwidth more than 6.5 GHz. The maximum 1-dB compressed output power (P1dB) is 12.2 dBm with a power-added efficiency at P1dB (PAE1dB) of 18% measured at 47 GHz; from 45 to 51 GHz, the measured P1dBis above 10 dBm under a 1.1-V supply voltage.
A 23-33GHz Transmitter in 65-nm CMOS Achieving 22.8-dB Gain and 14.5dBm Psat for 5G Communication System

This paper presents a transmitter applied to 5G cellular communication that operates from 23 GHz to 33GHz with the saturated output power of 14.5dBm. The transmitter with a two-stage power amplifier has the power gain over 22dB. The power amplifier is designed in a differential type with cross-coupled neutralized capacitors for stability and reverse-isolation improvement. Three transformers featuring different resonant frequencies are used to extend the bandwidth and ensure adequate output power. The on-chip transformer results in a high coupling factor to minimize the insertion loss. An interstage matching network between a mixer and a drive amplifier induces a channel flatness characteristic. With the power consumption of 112mW, the transmitter can reach the OP1dB of 6.2dBm. The designed transmitter is fabricated using a 65nm CMOS process and the chip size including pads and decoupling capacitors is 0.45mm× 1.18mm.
ROS Message Transport over Underwater Acoustic Links with ros_acomms

A software framework, “ros_acomms,” has been developed to enable transport of ROS messages and other data across low-throughput and high-latency underwater acoustic links. Messages are efficiently marshalled using user-provided configuration data, if available, or automatically via message introspection. A modular set of modem drivers, media-access-control engines, and message queues transport messages from one system to another via a modem. It supports message fragmentation, positive acknowledgment, and custody-transfer routing. It also includes an acoustic link simulator that uses a raytracing model to estimate link performance and latency. While it targets the WHOI Micromodem family of acoustic modems, the modular modem driver implementation has been leveraged to support low-throughput Iridium satellite links and other acoustic modems. It has been tested and used operationally at sea for remote redirection of autonomous underwater vehicles while providing operators with near real time vehicle telemetry and sensor data.
Perception vs. Reality: Understanding and Evaluating the Impact of Synthetic Image Deepfakes over College Students

Artificial Intelligence (AI)-powered Deepfakes are responsible for new challenges in consumers’ visual experience, and pose a wide range of negative consequences (i.e., non-consensual intimate imagery, political dis/misinformation, financial fraud, and cybersecurity issues) for individuals, societies, and organizations. Research suggested legislation, corporate policies, anti-Deepfake technology, education, and training to combat Deepfakes including the usage of synthetic media to raise awareness so that people can become more critical in detection when evaluating these contents in the future. To educate and raise awareness among the college-going students, this pilot survey study utilized both synthetic and real images over undergraduate students (N=19) to understand the human cognition and perception demonstrated by the literate population in detecting Deepfake media with their bare eyes. The results showed that human cognition and perception are insufficient in detecting synthetic media with their inexperienced eyes and even the intelligent population is vulnerable to this technology. While Deepfakes are becoming sophisticated and imperceptible, it was observed that this kind of survey study can be beneficial in raising awareness among the population about the societal impact of the technology and may also improve their detection ability for future encounters.