Miracle-dz/UniFAR-FTUs huggingface dataset:
Contact Tracing for COVID-19 among Health-Care Workers of a Tertiary Care Hospital in Mumbai

Background: Contact tracing (CT) is an effective tool for breaking the chains of transmission in infectious disease outbreaks. This study was conducted to observe the trend of isolation and quarantine, assess the source of infection and contacts, and assess the effectiveness of CT in the early detection of infection among health-care workers (HCWs). Methods: This study was conducted using secondary analysis of routine CT records of HCWs of a tertiary care hospital in Mumbai from April 9, 2020, to December 31, 2020. Details of all HCWs exposed or infected with COVID-19 were collected in a standard format developed for this purpose telephonically. The exposed HCWs were further divided into high-risk (HR)/low-risk (LR) contacts and quarantined. Results: A total of 744 HCWs were isolated during this period and 1486 contacts were quarantined against them. Majority of the HCWs affected from COVID-19 were resident doctors, interns, and nursing staff. More than 81% of the positive HCWs were symptomatic. The overall ratio between isolated HCWs and quarantined HCWs is 1:2. A total of 88 (6%) HCWs tested positive from quarantine. The test positivity rate among HR contacts was 9.01% and among LR contacts was 2.72%. Conclusions: Effective CT of positive HCWs greatly aids in the early identification of contacts and timely quarantine. Over a period of time, the number of HCWs getting isolated or quarantined is found to decrease. This is the true success of CT. This strategy can be implemented among other medical colleges and hospitals too.
Band alignment at the La2Hf2O7∕(001)Si interface

In the perspective of exploring alternative gate dielectrics for the future generation of microelectronic devices, we investigated experimentally and theoretically the interface energy barriers induced on (001) silicon by La2Hf2O7, whose growth has been recently attained by molecular-beam epitaxy. Experimental results show that the 5.6±0.1eV band gap of La2Hf2O7 is aligned to the band gap of silicon with a valence band offset of 2.4±0.1eV and a conduction band offset of 2.1±0.1eV. Density functional theory calculations yield valence band offset values ranging between 1.8 and 2.4eV.
A 60-GHz CMOS Power Amplifier with Combined Adaptive-Bias and Linearizer in 28-nm Process

This paper presents a 60-GHz CMOS power amplifier with on-chip combined adaptive-bias and linearizer (ABL) in 28-nm process. The proposed ABL adaptively compensates the impedance and gate bias of the proposed PA to expand P1dB with a low phase distortion and high back-off power efficiency. The fabricated PA with ABL with ABL on achieves a peak gain of 21 dB and a 3-dB bandwidth of 55.5 to 69 GHz. The PA with ABL on achieves a peak P1dB 12.5 dBm and PAE1dB of 20.5 % in the overall frequency band. Comparing the results of the PA with ABL off, improvements of 4.2 dB and 11 % are achieved with −2.3 dB insertion loss, respectively. The PA also achieves a peak PSAT of 15 dBm and PAEMax of 27.8%. The PA is implemented in a compact size area of 0.072 mm2 and the dc quiescent power consumption is only 62.4 mW.
Secure and scalable Blockchain-enabled traffic data management using a trust model

Most Intelligent Vehicles (IVs) can sense and share traffic conditions using embedded advanced sensing devices. However, the integrity, reliability, and availability of this data against potential attacks in the vehicular network remain a challenge. Recently, blockchain technology has been proposed to address these security concerns. Additional enhancements in the blockchain protocol are still needed to minimize the confirmation delay of traffic data. This work takes advantage of the traffic event validation context to improve the performance of the blockchain while main-taining the decentralization and security properties of the system. We propose a trust model that selects the most appropriate Road Side Units (RSUs) as event validators based on their reputation and distance from the event location. The proposed scheme was validated by simulation, and results showed good improvement in blockchain performance, decentralization, and fairness of the event validation process.
Application of Intelligent Transportation System Data using Big Data Technologies

Problems such as the increase in the number of private vehicles with the population, the rise in environmental pollution, the emergence of unmet infrastructure and resource problems, and the decrease in time efficiency in cities have put local governments, cities, and countries in search of solutions. These problems faced by cities and countries are tried to be solved in the concept of smart cities and intelligent transportation by using information and communication technologies in line with the needs. While designing intelligent transportation systems (ITS), beyond traditional methods, big data should be designed in a state-of-the-art and appropriate way with the help of methods such as artificial intelligence, machine learning, and deep learning. In this study, a data-driven decision support system model was established to help the business make strategic decisions with the help of intelligent transportation data and to contribute to the elimination of public transportation problems in the city. Our study model has been established using big data technologies and business intelligence technologies: a decision support system including data sources layer, data ingestion/ collection layer, data storage and processing layer, data analytics layer, application/presentation layer, developer layer, and data management/ data security layer stages. In our study, the decision support system was modeled using ITS data supported by big data technologies, where the traditional structure could not find a solution. This paper aims to create a basis for future studies looking for solutions to the problems of integration, storage, processing, and analysis of big data and to add value to the literature that is missing within the framework of the model. We provide both the lack of literature, eliminate the lack of models before the application process of existing data sets to the business intelligence architecture and a model study before the application to be carried out by the authors.
A Study of PWM Digital Technologies for Static Var Generator Based on FPGA Technology

Digital pulse width modulation can improve the output accuracy of PWM control signal, shorten the response time of SVG device, and increase the safety and stability of the system. Combined with the characteristics of the inverter circuit of high-power static var generator, this paper studies the digital theory of sinusoidal pulse width modulation (PWM). The full digitalization framework of field programmable gate array (FPGA) is constructed, and the realization method of regular sampling PWM digitalization is studied. This paper uses very high speed integrated circuit hardware description language (VHDL) and Xilinx XC6SLX16 FPGA chip to program the theoretical research content. The experimental results prove the correctness and feasibility of the digital theory in this paper.
Reductions for branching coefficients

Let $G$ be a connected reductive subgroup of a complex connected reductive group $\hat{G}$. We are interested in the branching problem. Fix maximal tori and Borel subgroups of $G$ and $\hat G$. Consider the cone $lr(G,\hat G)$ generated by the pairs $(\nu,\hat nu)$ of dominant characters such that $V_\nu^*$ is a submodule of $V_{\hat nu}$. It is known that $lr(G,\hat G)$ is a closed convex polyhedral cone. In this work, we show that every regular face of $lr(G,\hat G)$ gives rise to a {\it reduction rule} for multiplicities. More precisely, we prove that for $(\nu,\hat nu)$ on such a face, the multiplicity of $V_\nu^*$ in $V_{\hat nu}$ equal to a similar multiplicity for representations of Levi subgroups of $G$ and $\hat G$. This generalizes, by different methods, results obtained by Brion, Derksen-Weyman, Roth.
Self-organization and Nonuniversal Anomalous Scaling in Non-Newtonian Turbulence

We investigate through Direct Numerical Simulations (DNS) the statistical properties of turbulent flows in the inertial subrange for non-Newtonian power-law fluids. The structural invariance found for the vortex size distribution is achieved through a self-organized mechanism at the microscopic scale of the turbulent motion that adjusts, according to the rheological properties of the fluid, the ratio between the viscous dissipations inside and outside the vortices. Moreover, the deviations from the K41 theory of the structure functions’ exponents reveal that the anomalous scaling exhibits a systematic nonuniversal behavior with respect to the rheological properties of the fluids.
Realistic Behavior of Electrical Faults in $3-\Phi$ Induction Motor

The main objective of this research work is to visualize realistic behaviour of electrical faults in induction motor. Modified dynamic induction motor model with electrical faults are proposed. Analytical analysis and simulation studies will be carried using MATLAB/Simulink R2021a software. The proposed model can be used to test fault in the three phase induction machines and fault diagnostic algorithms.
Safe Reinforcement Learning for Urban Driving using Invariably Safe Braking Sets

Deep reinforcement learning (RL) has been widely applied to motion planning problems of autonomous vehicles in urban traffic. However, traditional deep RL algorithms cannot ensure safe trajectories throughout training and deployment. We propose a provably safe RL algorithm for urban autonomous driving to address this. We add a novel safety layer to the RL process to verify the safety of high-level actions before they are performed. Our safety layer is based on invariably safe braking sets to constrain actions for safe lane changing and safe intersection crossing. We introduce a generalized discrete high-level action space, which can represent all high-level intersection driving maneuvers and various desired accelerations. Finally, we conducted extensive experiments on the inD dataset containing urban driving scenarios. Our analysis demonstrates that the safe agent never causes a collision and that the safety layer's lane changing verification can even improve the goal-reaching performance compared to the unsafe baseline agent.
MIMU-M - A High Accuracy, Miniature INS based on GNSS and multiple MEMS IMUs

MIMU-M (MEMS based IMU Module) is an ITAR-free Inertial Navigation System (INS) which matches high performance into miniaturized dimensions (35 × 45 mm) and lower than 20gr weight. MIMU-M architecture is based on 4 MEMS IMUs, each one containing 3-axis gyroscopes and 3-axis accelerometers with a total of 24 axis, whose fusion provides the system orientation and rotation along the body axes. To enhance the IMU performance, single MEMS IMUs are subjected to a substantial screening process to preliminary discriminate anomalies and undesired behavior. The screened MEMS IMU is in-house fully calibrated with proprietary temperature and motion profiles to properly characterize the system in all operational environments. This calibration process, together with the IMU fusion, allows us to obtain exceptional results especially in terms of ARW (<0.17 deg/√hr) and gyroscope bias stability (< 0.7 deg/hr) that are essential requirements for navigation. The extracted compensation is embedded into the system through an ARM-based microprocessor, responsible also for the fusion algorithm. The MIMU-M includes a GNSS receiver, whose output is combined with the sensor block to estimate the hybrid INS output (attitude, heading, velocities and position) by means of a Kalman filter algorithm. In support of this, software real-time built-in tests are executed in the INS at power-on and continuously to check the correct working mode of the system. Moreover, a proprietary algorithm of IMUs fault detection (FDI) and recovery (FDIR) can be included to boost the system reliability in critical operations. This FDI algorithm identifies faults in navigation system caused by defective sensor (slowly ageing or suddenly broken) or variation in the operational environment that compromises the navigation model. This strategy is based on the placement of redundant IMUs (RIMU) which are used to isolate out-of-specification sensors allowing the system to continuously operate in a fail-safe mode (FDIR) with the remaining IMUs. MIMU-M system experienced an extensive test phase for validating data accuracies and testing the versatility in different environments. Successful land and flight tests were executed in different conditions included the most challenging that being GNSS outages. Test results showed that the MIMU-M offers excellent accuracy, especially on flight with errors (expressed as RMS) for attitude less than 0.2° and true heading accuracy drift equal to 1°/10min with GNSS aiding. Thanks to the combination of high performance and fault tolerance with the miniaturized volume and weight, MIMU-M aims at competitively occupying a high-level position into the MEMS INS product space.
PDF 2.0 CRACKING on CUDA

The latest graphic processor provides high processing power, and research using the graphic processor for cryptographic processing is being actively conducted through the groundbreaking performance of the GPU. PDF (Portable Document Format) is a file format developed by Adobe in 1992, standardized as ISO 32000 and used worldwide. Mainly used files such as PDFs can be subject to password cracking. In this paper, an optimal implementation on CUDA GPU is implemented for PDF 2.0 cracking. Optimization of the AES and RSA-2 algorithms used for cracking was optimized. In addition, CUDA GPU shared memory and command functions were used. As a result, we achieved the performance of calculating approximately 76,405 passwords per second in the RTX 3060 environment.
A Dual-Band Millimeter Wave SRR Loaded Printed Monopole with Annular Slot MIMO Antenna for 5G Applications

This paper presents a two element dual-band multiple input multiple output (MIMO) antenna for millimeter-wave (mm-wave) applications at 28 and 38 GHz frequency bands. The proposed design consists of a printed monopole antenna with a split ring resonator (SRR) geometry in the top layer and an annular slot in the defected ground plane. A single antenna element along with the connector model has initially been designed with a detailed analysis and subsequently, the two-port MIMO antenna with connectors has been realized. By precisely optimizing the design parameters, the geometry exhibits broadside radiation performance at 28 GHz and 38 GHz. A considerable gain and wide impedance characteristics are also observed in both bands for both the antennas. Several MIMO parameters, such as envelope correlation coefficient, total active radiation coefficient, and channel capacity loss are also calculated, and the results are suitable for 5G mobile applications. Finally, both the antenna prototypes are fabricated and their results are in close agreement with the simulated responses, validating the proposed concepts
A Simulation System for Human-in-the-Loop Driving

By combining driver's ability with the machine intelligence, we are trying to augment the system with the combination of both roles during driving. The human-in-the-loop hybrid-augmented system is expected to monitor and model drivers' behavior; while the current datasets for driving mainly focus on the data of scene. The lack of the driver's data restricts the capability to understand driver's inner state. In the paper, we supply an effective way to monitor the driver and construct a simulation system for the human-in-the-loop autonomous driving, which realizes the modeling of driver's behavior. Based on it, we provide a data acquisition scheme for both driver's behavior and scene content and subsequently construct the corresponding dataset. For the simulated driving dataset, we provide a method for evaluating the human-in-the-loop-system, which can help the community to develop such human-in-the-loop systems.
Correlation functions and renormalization in a scalar field theory on the fuzzy sphere

We study renormalization in a scalar field theory on the fuzzy sphere. The theory is realized by a matrix model, where the matrix size plays the role of a UV cutoff. We define correlation functions by using the Berezin symbol identified with a field and calculate them nonperturbatively by Monte Carlo simulation. We find that the 2-point and 4-point functions are made independent of the matrix size by tuning a parameter and performing a wave function renormalization. The results strongly suggest that the theory is nonperturbatively renormalizable in the ordinary sense.
Blue Danube: A Large-Scale, End-to-End Synchronous, Distributed Data Stream Processing Architecture for Time-Sensitive Applications

An extensive list of time-sensitive applications requiring ultra-low latency ranging from a few microseconds to a few milliseconds are presented in recent publications and IEEE standards. Time-sensitive applications, include industrial, critical healthcare and transportation applications as also applications for Smart Grids and the Internet of Vehicles – one of the most active research fields of Intelligent Transportation Systems of Smart Cities. In this work, we mainly set our focus on the suite of safety applications which attracts strong interest from the research community, as it aims to avoid road accidents and save lives. The IEEE Time-Sensitive Networking (TSN) set of standards specifies fundamental real-time characteristics. Nevertheless, as TSN works on Data Link layer (Layer 2 of the OSI model) the benefits of these characteristics fade away when other layers are crossed from the Application layer (Layer 7). Indicatively, recent research works report latencies on the order of tens of seconds when benchmarking Data Stream Processing and IoT platforms, and thus they are not suited for time-critical applications. Such platforms mainly use loosely coupled components with asynchronous communication. On Application layer, we propose a novel End-to-End Synchronous, Distributed Architecture for Large-Scale, High-Bandwidth, Ultra-Low Latency Data Stream Processing. Through our Big Data Stream analysis experiments (4.7 Gbit/s total average aggregated throughput, 1 Terabyte in-memory distributed database, 4 milliseconds average query latency) we have demonstrated the suitability of our architecture for time-sensitive applications such as accident avoidance for the Internet of Vehicles.
Dynamic asymptotic homogenization for periodic viscoelastic materials.

A non-local dynamic homogenization technique for the analysis of a viscoelastic heterogeneous material which displays a periodic microstructure is herein proposed. The asymptotic expansion of the micro-displacement field in the transformed Laplace domain allows obtaining, from the expression of the micro-scale field equations, a set of recursive differential problems defined over the periodic unit cell. Consequently, the cell problems are derived in terms of perturbation functions depending on the geometrical and physical-mechanical properties of the material and its microstructural heterogeneities. A down-scaling relation is formulated in a consistent form, which correlates the microscopic to the macroscopic transformed displacement field and its gradients through the perturbation functions. Average field equations of infinite order are determined by substituting the down-scale relation into the micro-field equation. Based on a variational approach, the macroscopic field equations of a non-local continuum is delivered and the local and non-local overall constitutive and inertial tensors of the homogenized continuum are determined. The problem of wave propagation in case of a bi-phase layered material with orthotropic phases and axis of orthotropy parallel to the direction of layers is investigated as an example. In such a case, the local and non-local overall constitutive and inertial tensors are determined analytically and the dispersion curves obtained from the non-local homogenized model are analysed.
Semi-analytical Modelling of Boundary-tunable Metasurface Antenna for Compressive Sensing

A proof-of-concept semi-analytical demonstration is presented for practically implementable boundary-tunable metasurface antenna. Proposed antenna consists of reverberating planar parallel-plate waveguide with two via layers on the boundary. Different radiation pattern can be generated by electronically switching vias in the inner boundary layer. This arrangement allows to select different measurement modes necessary for interrogating a scene in the computational imaging framework. The advantage of this design is increased beam diversity compared to the passive frequency tuned approach while maintaining a planar configuration. The performance of the antenna is verified by analysing the singular values of the waveguide modes inside the antenna.
Automatic Multi-camera Multi-LiDAR Extrinsic Calibration using Geometric Corners

In autonomous driving systems, multiple sensors are installed to provide complementary information. Extrinsic calibration is the prerequisite of multi-sensor fusion, which is critical to the accuracy and performance of the fused system. In this paper, we propose a unified algorithm for extrinsic calibration among multiple cameras and light detection and ranging (LiDAR) sensors using only a standard checkerboard. The geometric information of the checkerboard, containing plane coefficients, boundary points and geometric corners, is extracted automatically with a lightweight approach by leveraging the characteristics of mechanical LiDARs. A coarse-to-fine manner is adopted in the extrinsic calibration by utilizing these features in different stages. Our algorithm has been experimentally validated through simulations and real-world experiments. The experimental results show that the proposed method is capable of providing accurate extrinsic parameters and is robust in different levels of Gaussian noise. All procedures in our approach operate automatically, without any manual intervention, which is beneficial to practical applications.
Infant Facial Expression Recognition Based on Parameter-free Attention Module

In order to further improve the accuracy of infant facial expression recognition, an infant facial expression dataset was established, and a parameter-free attention module (PFAM) was proposed. Firstly, the images about infants were collected through the Internet. After screening, 17785 images were chosen and divided into five categories, namely happiness, sadness, surprised, sleeping, and neutral, which generally reflect the infant facial expression. Secondly, using the average pooling and max pooling characteristics in the feature map channel and space, we proposed the parameter-free attention module. Finally, the recognition rate was compared to the common attention module and the deeper residual network. The experimental results show that the recognition rate of Resnet18 network with the PFAM is superior to attention modules SE and CBAM and deeper residual networks, and the recognition rate on self-built infant facial expression dataset exceeds that of the ResNetl01, and the recognition rate on public facial expression dataset RAF-DB exceeds that of the ResNetl52.
Flat Hyperbolic Lens antenna in Gap Waveguide at 300 GHz

In this paper, a flat hyperbolic lens-corrected H-plane horn antenna is designed at 300 GHz using Groove Gap Waveguide (GGW) technology. A GGW horn antenna is employed to feed the metamaterial lens placed in a parallel plate waveguide (PPW), in order to increase the directivity in the direction of propagation. Both devices, the metalens and the GGW antenna, achieve excellent radiation results when combined together.