Miracle-dz/UniFAR-FTUs huggingface dataset:
Low- Profile, Microstrip-Fed Slot Antenna for NR77/78 5G and WiMAX Applications

A novel VI slot microstrip-fed antenna is designed for NR 77/78 5G and WiMAX application for wireless operation. The innovation of the proposed antenna is the comprehensible structure which does not use any reactive elements, costly substrate, supplementary hardware and 3D structures. The proposed structure has designed footprint of only 18 × 8.8 × 1.6 mm3 and engraved on FR-4 substrate having relative permittivity 4.3 and loss tangent 0.025. Moreover, microstrip fed results in single resonance of (3.06-4.16 GHz) with simulated impedance bandwidth of 33.3%. The omnidirectional 2D pattern followed by gain of around 1dBi and efficiency values of approx. 60%, exemplify the pertinency of the antenna for wireless operation.
An Improved IoT Framework Data Transmission and Security using Light Weight Authenticated Key Management Mechanism

Internet of Things (IoT) is also known as an interconnected network between people and objects, which provides various services and fulfill the role for distinct applications. The main aim of IoT system is to restructure various lives in order to evolve and perform the tasks around different ways. IoT has many robust and scalable applications, which are used from basic user needs to industrial requirements to achieve faster communication between useis and objects. The researchers are combining IoT with Artificial Intelligence in cloud to form smart world and has served billions of devices. These devices can operate in distinct scenarios like environmental monitoring, smart grid, industrial intelligence, and were interconnected to central clouds. The central clouds have the ability to operate with powerful intelligent processing. The high complexity is created for privacy preserving operations by using encryption algorithms and it ensures the security of the acquired data during the transmission over internet. The structure of IoT is required to be improved to secure the data transmission due to the multiple challenges such as the usage of huge data, effective resource allocation. Therefore, it is essential to develop an efficient method to ensure the security with integrity to provide diagnostic information during transmission and reception of data in an IoT environment. The Light Weight Authenticated key management mechanism with edge based IoT environment (LWAKM-EIoT) is proposed in this research to evaluate False Positive Rate (FPR) which is an important parameter to determine the data transmission rate. The proposed LWAKMEIoT showed 2% of the improvement in FPR when compared to the existing Communicating Things Network (CTN) environments.
Interpretable Natural Language Segmentation Based on Link Grammar

Natural language segmentation (NLS), or text segmentation, refers to the process of dividing written text into meaningful units. Sentence segmentation, a subfield of text segmentation, is the problem of dividing a string of natural language text into its component sentences. Current methods of sentence segmentation are often either hardcoded—they require manual implementation of fixed grammar and segmentation rules—or require extensive training on labeled corpora and are not explainable—they are "black box" algorithms that cannot be understood by humans. In this paper, we present a novel explainable sentence segmentation method capable of separating bodies of text into grammatically valid sentences solely based on the grammatical relationships between individual words or tokens. The proposed NLS architecture can both automate the input query parsing and semantic query execution components of voice-activated question answering and information retrieval systems as well as enable automatic summarization, entity extraction, sentiment identification, and a variety of other natural language processing (NLP) algorithms that operate at the sentential level.
Analysis of Torque Ripple in Vector Control of BLDC Motor using SVPWM Technique

Industrial generally needs low oscillations in motor output. As compared to other motors the BLDC motor has more advantages and used in more applications. The torque ripples and speed control in motors are playing a key role in application. So definitely, there is a need to reduce the oscillations and control the speed in closed loop operated BLDC motors. This paper presents the PI, fuzzy controller for reducing the torques ripples and also for speed control of BLDC motor. Pulse width modulation (PWM) generation and subsequent switching can reduce oscillations in torque content. For accurate results mathematical modelling is required to design in simulation model. The simulation models of mathematical modeling, Space vector Pulse width modulation operated BLDC with PI & fuzzy control and the results are also presented.
A 1∼4GHz Tri-Directional RF Amplifier Based on CMOS Technology for Digital Array Radars

This paper presents A 1 to 4 GHz tri-directional RF amplifier based on 0.18um technology for digital array radar applications. A wideband matching from 1 to 4 GHz at RF port is achieved while the circuit is working in both TX and RX mode. The circuits consist of a variable gain low noise amplifier, a drive amplifier and a single pole double throw switch. The circuit shows gain from 7.6 to 14.2 dB, and the noise figure is less than 5.3 dB with −3dBm@2.4GHz input P-1dB compression point in RX, while gain of 9.9dB and output P-1dB compression point is 11.4dBm@2.4GHz in TX. The chip size is 2.25mm*0.97mm (with pads).
Energy Management via Passivity-Based Control for Battery/Supercapacitor Electric Vehicles

A battery/supercapacitor electric vehicle is a promising configuration for vehicles with zero emissions and high efficiency. The propulsion system consists of an electric motor and the energy storage system is composed of batteries and supercapacitors. The electric vehicle is a power system in which the motor and the storage devices are coupled to a common DC bus through power converters. A proper energy management of the energy storage system is required to meet the power required by the electric motor. Since the storage devices possess complementary electric characteristics, it is expected that the controller achieves the main task of satisfying the demanded energy, exploiting these features to get substantial benefits in the vehicle driving performance and the battery life. In this article, we propose an energy management designed via passivity-based control for a battery/supercapacitor electric vehicle. The controller commands the energy storage system to satisfy the electric motor demand, reducing the high peak power undergone by the battery through the high power capacity of the supercapacitors. The methodology is tested under several demanding driving cycles and the results show that the battery life could be significantly extended.
Drug-Drug Interaction Extraction using Pre-training Model of Enhanced Entity Information

It is a common phenomenon to use two or more drugs during the treatments. However, a safety concern, drug-drug interaction (DDI), might occur which will cause unexpected damage to patients. Therefore, how to avoid DDI has been a major consideration all along the drug development. This paper will present a new model for extracting DDI from biomedical literature called Bio-ER-BERT, which combines the characteristics of the pre-training model BioBERT for biomedical text mining and the R-BERT model for relationship extraction. To improve the initial performance of the model, BioBERT model is introduced as the pre-training model in this paper. Meanwhile, to enhance the accuracy of DDI extraction, the R-BERT model is improved by changing the approach it obtains embedded information about two drug entities from averaging operation to long short-term memory network (LSTM). According to the experiments carried out on DDIExtraction 2013 dataset, the F1-score of our model reaches 83.88%, which has an obvious better performance comparing to the existing models such as SCNN and AB-LSTM.
Modelling and Simulation of Electric and Hybrid Electric Vehicles

This paper discusses the modelling and simulation of electric and hybrid vehicles. Modelling method for electric and hybrid vehicle is discussed with powertrain components. Genetic Algorithm is used as optimization strategy for optimization of powertrain components and control system for fuel economy and reduction of the emission. The ADVISOR software developed by NREL is used for carrying out the simulation. The simulation results demonstrate that optimal selection of parameters and a decision can be made for satisfactory solution in conformity with statutory requirements for emission of electric and hybrid electric vehicle in this paper.
Insertion of Ag Layer in TiN/SiNx/TiN RRAM and Its Effect on Filament Formation Modeled by Monte Carlo Simulation

In this study, the electrical characteristics of TiN/SiNx/TiN and TiN/Ag/SiNx/TiN RRAMs were thoroughly investigated through I-V measurements. Our novel Ag-inserted silicon nitride-based resistive switching memory (RRAM) achieved switching operation at lower voltages and lower current levels compared to the conventional silicon nitride RRAM that does not have an Ag insertion layer. These enhanced characteristics enable low power operation, one of the main goals of RRAM research. Gradual conductance modulation is also possible in this device with constant voltage pulses being 50% lower than that seen in conventional devices, this property fulfills an important requirement that makes our device suitable for use as a neuromorphic synapse device. The conduction mechanism of our Ag-inserted device was further analyzed through I-V measurements and compared with a control group. Based on these data, a 2D Monte Carlo simulation was implemented to investigate the mechanism behind the new behavior displayed by our Ag-inserted silicon nitride RRAM. A resistive network model was used to calculate the voltage and electric field distribution, and then the motion of the particles was simulated by taking into account its relation to the heat generated inside the device.
Communication and Computation Resource Allocation and Offloading for Edge Intelligence Enabled Fault Detection System in Smart Grid

Smart grids have various capabilities to meet the electricity demands of modern society in production and life, and real-time monitoring of smart grids is critical to enhance the reliability and operational efficiency of power utilities. With the development of artificial intelligence technology and cloud computing, several studies have proposed using the powerful computing ability of clouds to design fault detection systems based on deep learning. However, due to the transmission delay in the Internet backbone and the large amount of data uploaded to the system, various problems arise such as a large bandwidth load and poor real-time feedback from the cloud platform. To solve these problems, it is necessary to embed the artificial intelligence technology at the edge of a network to realize a de-centralized system. In this paper, we propose an edge computing assisted smart grid fault detection system that uses an embedded lightweight neural network device, which is placed near the edge of the monitored equipment to realize real-time monitoring. In addition, considering the limited communication resources, relatively low computation capabilities, and different monitoring accuracies of edge devices, we design an optimal allocation method for communication and computation resources, which can maximize the throughput of the system, and improve the resource utilization of the system while meeting the requirements of data transmission and delay processing. Finally, simulation experiments are carried out to show that compared with other structures of smart grid fault detection systems, our proposed system can transmit more data while meeting the requirements of the delay bound, reduce the time required for transmission, and enhance the real-time performance of smart grid fault detection systems.
An Energy-Efficient Time-Domain Binary Neural Network Accelerator with Error-Detection in 28nm CMOS

Due to the increasing demand of high energy-efficient processor for deep neural networks, traditional neural network accelerators with high-precision weights and activations that usually occupies huge on/off-chip resources with large power consumption is no longer suitable for internet-of-things applications. Binary neural networks (BNNs) reduce memory size and computation complexity, achieving drastically increased energy efficiency. In this paper, an energy-efficient time-domain binary neural network accelerator is optimized for image recognition, with time-domain accumulation (TD-MAC), timing error detection based adaptive voltage scaling design and the related approximate computing. The proposed key features are: 1) an error-tolerant adaptive voltage scaling system with TD-MAC chain truncation for aggressive power reduction, working from near-threshold to normal voltage; 2) architectural parallelism and data reuse with 100% TD-MAC utilization; 3) low power TD-MAC based on analog delay lines. Fabricated in a 28nm CMOS process, our timing error detection based adaptive voltage scaling design enables the whole system achieves a maximum 51.5TOPS/W energy efficiency at 0.42V and 25MHz, with 99.6% accuracy on MNIST dataset.
Wireless radio resource management using evolutionary computation in CDMA cellular radio networks

- This paper has proposed a distributed power control algorithm that employs a evolutionary computation technique in order to solve linear systems of equations for power update in CDMA cellular radio systems. The proposed algorithm is developed by applying Bremermann 's evolutionary computation algorithm to the CDMA power control problem. The major gain from the applied evolutionary computation algorithm is more rapid optimization on linear systems of equations compared with the simple genetic algorithm (SGA). Employing the distributed constrained power control (DCPC) as a basic reference algorithm, we have designed and implemented computational experiments on the DS-CDMA system. The results indicate that the proposed algorithm significantly enhances the optimization and calculation speed of power control. The proposed algorithm is also compared with the bang-bang type algorithm used in the IS-95 and the W-CDMA systems. The results show that our proposed algorithm also has a high potential advantage for increasing the CDMA cellular radio network capacity and decreasing the mobile terminal power consumption.
Influence of Device Parameter Variability on Current Sharing of Parallel-Connected SiC MOSFETs

In this paper, impact of device parameter variation on current sharing between silicon carbide (SiC) metal-oxide-semiconductor field-effect transistors (MOSFETs) connected in parallel has been studied via Monte Carlo simulation. Paralleled MOSFETs driving an inductive load in a switching circuit, which are expressed by using a surface-potential-based SiC MOSFET model, have been analyzed. From the simulation results, dominant device parameters that affect the mismatch in the SiC MOSFET currents are identified. We also evaluate the effect of current mismatch on the energy loss imbalance. In our analysis, the model parameters related to flat-band voltage, channel length modulation, and current gain factor are found to be particularly important.
Extending Boolean Methods for Scalable Logic Synthesis

In recent years, Boolean methods in logic synthesis have been drawing the attention of EDA researchers due to the continuous push to advance quality of results. Boolean methods require high computational cost, as they rely on complete functional properties of a logic circuit (e.g., don’t cares), but usually result in better optimization. In particular, Boolean resubstitution is considered one of the most powerful Boolean methods in logic synthesis. In this paper, we present three novel Boolean resubstitution algorithms designed to be scalable and runtime-effective in a modern synthesis flow. They make use of circuit partitioning techniques and Boolean filtering to be fast and computationally tractable. We also discuss different data structures and reasoning engines, namely truth tables, binary decision diagrams, and satisfiability, that are required to gather don’t cares and functional information. As the choice of the engine determines the scalability of Boolean resubstitution we present different scenarios in which the Boolean methods are best driven by one or the other of these. We have implemented the presented resubstitution techniques together with state-of-the-art methods in an industrial logic optimization engine to create a novel resynthesis flow. Our global resynthesis flow achieves significant synthesis results: Within the EPFL synthesis competition, we improve the best-known area results when mapped into LUT-6; when embedded in a commercial EDA flow, the new Boolean resynthesis flow results in 3.12% combinational area savings and 1.34% WNS reduction after physical implementation, at contained (w.r.t. the time of the entire flow) runtime cost.
“CNN based Cognitive Spectrum Sensing with Optimization”

Spectrum detection is remaining as a key innovation for intellectual radios. This research work presents the range detecting as an order issue and propose a detecting technique dependent on deep learning CNN characterization. The proposed research work will standardize the got signal capacity to conquer the impacts of clamor power vulnerability. The proposed model is trained with whatever number sorts of signs as could reasonably be expected just as commotion information to empower the prepared system model to adjust to undeveloped new signals. Likewise, it will use move learning methodologies to improve the presentation for certifiable signs. Broad examinations are led to assess the exhibition of this strategy.
State Estimation and Control for Micro Aerial Vehicles in GPS-denied Environments

In this paper, a state estimation and control system in GPS-denied environments is presented for a micro aerial vehicle (MAV). For full state estimation, we formulate the lidar-inertial odometry as a factor graph which can solved by a nonlinear optimizer. For hovering and trajectory control, we propose a dual-mode cascaded nonlinear controller, which ensures the hovering accuracy and trajectory tracking performance. In extensive state estimation and flight tests, we evaluate the performance of the developed control and estimation algorithms proposed in this paper on a onboard computer.
Hybrid Stochastic Ranking for Constrained Optimization

Teaching learning based optimization (TLBO) algorithm is a distinguished nature-inspired population-based meta-heuristic, which is basically designed for unconstrained optimization. TLBO mimics teaching learning process through which learners acquire knowledge from their teachers, and improve their results/grades, accordingly. Stochastic ranking (SR) is a constrained handling technique (CHT), which produces greediness among solutions to improve their fitness values and feasibility. Violation constraint handling (VCH) technique produces more feasibility among the existing superiority of feasibility CHTs due to its additional factor of ranking based on the number of constraints violated (NCV). This work brings in a new variant of SR, namely hybrid stochastic ranking (HSR), which combines SR and VCH. For constrained optimization, the integration of some CHT with TLBO is essential. In this paper, HSR is integrated with TLBO and a new constrained version of TLBO called HSR-TLBO is designed. The efficiency of HSR-TLBO is checked on constrained test functions of the suit CEC 2017. The experimental results show that HSR-TLBO got prominent position when compared and ranked with the top four papers and our two newly designed constrained variants of TLBO, MSR-TLBO and MVCH-TLBO, based on the provided budget and ranking criteria of the mentioned suit.
Bidirectional Perspective with Topic Information for Stance Detection

Because of the convenience of the Internet, there are many websites or online news spread misinformation, cause panic and trepidation in society. Automatic fake news detection can classify fake news and help the society to clarify the information is true or false without human checking. Detecting fake news by analyzing the stance is one of the mainstream methods, stance detection has become a new popular research field in recent years. How to accurately detect stance has become the primary goal of detecting fake news. This research aims to detect the news stance accurately, and we propose a method based on a pre-trained BERT language model. Most of the previous work only used the knowledge of single inference direction when classifying the stance, which may lose some important information. Therefore, we propose a bidirectional inference stance detection model, which can leverage bidirectional perspective information to classify the stance more comprehensively. We also define the stance detection task as a hierarchical structure task, and use the hierarchical classification and incorporate the topic information to help the stance classification. Experiment results show that our model can classify the stance more accurately.
Automatic Frequency Calibration Module based on High-speed Counter

An automatic frequency calibration (AFC) based on high-speed counter is proposed for all-digital phase-locked loop(ADPLL). The AFC module is designed for coarse and medium calibration. The calibration mode is updated after the frequency difference is 0. For the frequency difference is accurate, the AFC adopts double-edge counting and satisfies the relation of real frequency and target frequency. The AFC and counter circuit implemented in a 40nm CMOS process occupies $2358.8\mu\mathrm{m}2$. Under the condition that the reference frequency is 100MHz and the DCO output frequency is 1GHz, the calibration needs about $5\mu\mathrm{s}$ in the best case, and $21\mu\mathrm{s}$ in the worst case.
A Review on the Generalized Formulations for Selective Harmonic Elimination (SHE-PWM) strategy

Better harmonics spectrum due to efficient elimination of dominant harmonics, Selective Harmonic Elimination (SHE-PWM) method has been extensively preferred for optimal control of multilevel inverters (MLI's). However, switching angle solution depends on the proper SHE problem formulation and solving techniques adopted. Various aspects which determines the formulation complexity such as nature of the waveform, number of step switching's, symmetry and voltage level characteristics has been discussed in this paper. Simulation results for multiple solution trajectories pertaining to single as well as multiple step switching transitions are evaluated using hybrid optimization method and presented for readers to attain better understanding of the SHE formulations and its related solutions.
Suraksha: Low Cost Device to Maintain Social Distancing during CoVID-19

On the 11th of March 2020, the World Health Organization (WHO) affirmed COVID-19, a pandemic, in response to more than 1,00,000 confirmed cases globally in more than 100 nations and the persistent danger of spreading further. Presently, there’s no remedy or vaccine for COVID-19. The only way to curb its menace is taking precautionary measures, as advised by the health experts. Social distancing, that is maintaining a minimum distance of 1 to 1.5 meter between two individuals, is one of the foremost proactive measures advised by WHO. This paper presents a smart wearable device, Suraksha, that can be worn while travelling outside and will help maintain social distancing. The user will be able to rely on this device’s alerting mechanism without having to constantly worry about their surroundings. It is a simple device which is easy to use and is built using basic electronic components. The device is also capable of integrating with the health applications over Bluetooth and support contact tracing.