Miracle-dz/UniFAR-FTUs huggingface dataset:
EDITH - A Robust Framework for Prevention of Cyber Attacks in the Covid Era

In the era of cloud technology, all the emails sent from one person to another needs to be secure. Security attacks like email phishing, EBomb, DNS spoofing and so on have become a trend in this digital era. Among the said attacks, email phishing has been observed to be most common to be used by attackers to trap victims using fake email with embedded malwares, which is not made aware of non-cyber professionals. With the current pandemic that is Covid-19, which is storming the whole world and enforcing people all over to stay indoors, thus indirectly increasing the online digital footprints of all, so there’s an increase in SMB(Server message block) port all over the world which is leading attackers to find their victims easily by unique, dynamic and various other vulnerabilities which no standard virus, malware detection software which was before being provided by the IT companies to its employees and for general internet users; have proven to be not that effective. so we propose a different approach along with a tool kit that will work in identifying the embedded malware files and fake websites more dynamically and effectively.
A Bootstrapping Approach to Optimize Random Walk Based Statistical Estimation over Graphs

Graphs are commonly used in various applications such as online social networks (OSNs), E-commerce systems and social recommender systems. Random walk sampling is often used to conduct statistical estimation over such graphs. This paper develops an algorithmic framework to reduce the mean square error of such statistical estimation. Our algorithmic framework is inspired by that the mean square error can be decomposed into a sum of the bias and variance of the estimator. More specifically, we apply the bootstrapping technique to design a bias reduction algorithm. A new feature of this bias reduction algorithm is that it allows the variance to increase whenever the bias can be further reduced. The increased variance may lead to a large mean square error of the estimator. We use multiple parallel random walks to reduce this variance such that it can be reduced to arbitrarily small by deploying a sufficient number of random walks. Our algorithmic framework enables one to attain different trade-offs between the sample complexity (i.e., number of parallel random walks) and the mean square error of the statistical estimation. Also, the proposed bias reduction algorithm is generic and can be applied to optimize a large class of random walk sampling algorithms. To demonstrate the versatility of the framework, we apply it to optimize the Metropolis random walk and simple random walk sampling. Extensive experiments confirm the effectiveness and efficiency of our proposed algorithmic framework.
Research on Passenger Flow Path Reduction of Urban Rail Transit Based on AFC Data and Transfer Estimation

In this paper, the acquisition of AFC system in the data of total passenger travel time into walking time, the train running time, train time, transfer time and waiting time to five parts, using the mean estimate the five unknown variables, at the same time using the A * pathfinding algorithm traverses all paths between the OD and calculate the travel time of the theoretical value, the closest to real passengers travel long is one of the most matching paths. The research on the passenger flow path reduction model of urban rail transit network helps managers master the real-time distribution of passenger flow in the network and further improves the quality of the train operation plan of urban rail transit network.
Comparison of Multimodal Features for Detecting Driver Drowsiness using different dataset

Road accidents are increasing day by day. One of the major causes of road accidents is drowsiness and distraction of the driver. Due to human faults, a greater number of death and injuries occurs around the world. Drowsiness and distraction while driving is now identified as one of the reasons behind fatal crashes and highway accidents caused by drivers. If an alert is given before the driver becomes drowsy the number of road accident can be prevented and crashes can be avoided. This paper mainly focuses on different drowsiness methods to detect driver's drowsiness, driver's fatigue and driver distraction. This paper conducts a detailed study on different methods such as the Behavioural method, Vehicle method, Physiological method and Hybrid method that relate to drowsiness, fatigue and distraction. The purp ose of the study is to identify the significant features from the different methods to detect driver drowsiness. A study is based on Different datasets like YawnDD and NTHU-DDD, Different methods like Behavioural methods, Vehicle method, Physiological method and Hybrid method with different features like Eye state like Blinking, PERCLOS, Mouth state like Yawning, wearing glasses and illumination conditions and different classifiers like Support Vector Machine (SVM), Convolutional Neural Network (CNN) are compared to predict the best dataset and best features for monitoring driver drowsiness. compared to the multimodal features like PERCLOS and Blinking, Yawning and Blinking produces 85%-99% of accuracy other than the single features like PERCLOS, Blinking, Yawning produces around 75%- 90% of accuracy.
Simultaneous Measurement of High Refractive Index and Temperature Based on SSRS-FBG

In this study, a novel and simple optical fiber sensor is proposed, simulated, and experimentally demonstrated for simultaneous measurement of high refractive index (RI) and temperature. The sensor consists of a section of single-mode-silica rod-single-mode (SSRS) fiber structure cascaded to fiber Bragg grating (FBG) (SSRS-FBG). The simultaneous measurement of high RI and temperature was realized in this study by monitoring the output power level and wavelength shift of the SSRS-FBG single dip transmission spectrum corresponding to different surrounding high RI and ambient temperature values. The experimental results showed that the cascaded sensor had a sensitivity of 108.07 dBm/RIU for high RI ranging at 1.45-1.531, while the temperature sensitivity was 9.31 pm/°C from 35 °C to 85 °C. This sensor is most suitable for high RI applications, such as for monitoring oil quality stored in terminal and power transformers.
Performance Analysis of Pre-Processing techniques in Plantain Tree Disease Classification with Convolutional Neural Network

The agricultural sector is considered as the backbone of almost all economies present in the world. Crop protection is remaining as a crucial factor in agriculture, since it determines the quantity and quality of products and increases the profit of the farmers. In the Indian economy, plantain tree cultivation is playing a major role. Plantain Tree is highly exposed to diseases such as Black Sigatoka/Yellow Sigatoka, Panama, Bunchy top, Moko, chlorosis, etc. This study aims at developing a DL model to facilitate crop protection by accurately identifying the diseases that affect the productivity of plantain trees by using the leaf images. The pre-processing of image data will also help to improve the classification accuracy and training time. The main aim of this study is the research investigation and performance comparison of various data augmentation and feature selection techniques that helps in the classification and detection of diseases in plantain trees. The experiments are carried out in real-time datasets, which are collected from different plantain tree cultivation in different fields of Tamil Nadu. The experimental results reveal the necessity to propose effective preprocessing techniques for improving the accuracy of the classification models.
Comparitive Study of Conventional Inverter Topologies for Stand-Alone PV System

Multilevel converters, generally, used in medium and high-power system applications, of any configurations, offer higher efficiency and lower THD. With the advance of renewable energy systems, particularly photovoltaic (PV) systems, there is always an ever-increasing need for highly efficient multilevel inverters to cater the AC loads. The present work aims at comparing the basic three inverter topologies [Diode clamped, Flying-Capacitor, Cascaded H-Bridge inverter] of five-level inverter for a stand-alone PV-system. The efficiency and harmonic distortion of these configuration are compared. The entire work is carried out in MATLAB Simulink Environment.
Mobile User Identification across Domains Based on the Stacking Method in Ensemble Learning

Given the behavioral data of a mobile device, and finding similar devices among massive candidates, this is actually a user identification problem using mobile device data. This problem is essential in many practical applications, such as authorized user identification, personalized recommendation and privacy protection. Most of the previous work in this problem only used the data from a single domain, such as trajectory data or app data, and most of their experimental datasets are in an ideal environment with a small number of users, high sampling rate and high accuracy. However, it is often difficult to obtain this kind of data in real-world activities. To address these limitations, we introduce a new framework for mobile user identification, divided into a recall module and a sorting module. In the sorting module, we design and combine multiple similarity comparison algorithms based on stacking method in ensemble learning. We evaluate the framework performance on a realworld anonymous dataset from numerous mobile devices, containing the behavioral data across several domains but with a low sampling rate and accuracy. Our framework achieves an AUC of 0.917 and F1 score of 0.839 which outperforms the state-of-the-art methods.
Performance Enhancement of PI-Controller Using SVM for DFIG-Grid Interconnected System

The advantages of reactive power compensation can be insured by properly placing the STATCOM in a wind integrated power system. The modern power system is highly scattered and STATCOM is a shunt compensator and it is used in large transmission lines for providing power quality compensation. Optimal placement of facts devices can be achieved by applying a stochastic algorithm which is a very diverse and continuous operation to the algorithm is going on. In this research work, multiple optimal solutions had been performed with a multi-objective problem to provide some flexibility for making a perfect decision about the placement of STATCOM. Successful implementation of STATCOM leads to a reduction in the power system losses, improvement of bus voltage, stability enhancement of voltage in terms of the optimization algorithm.
Design and Simulation based Analysis of Flexible Capacitive Pressure Sensor and Improvement in Sensing Characteristics

In this paper, an ultra-small MEMS-based flexible capacitive sensor is proposed. Finite element analysis (FEA) simulation tool is used to design and analyze the sensor performance for a uniaxial uniform pressure range of 0-50 kPa applied to sensor geometry’s top surface. The sensor characteristics such as a change in sensor capacitance, total surface displacement with external pressure effect, capacitance relative change with pressure are analyzed with the finite element method. The sensor’s obtained result for flat-panel dielectric structure exhibits a linear response for a pressure range of 0-50 kPa and sensitivity of $0.63 \times 10^{-3}$ fF/ [kPa]. However, to improve the sensitivity of sensor micro cuboid and truncated pyramid structured dielectric layer is introduced, which results in effectively enhanced sensor characteristics. The sensor shows a linear response with improved sensitivity of $16.8 \times 10^{-3}$ fF/ [kPa] for cuboid and $19.4 \times 10^{-3}$ fF/ [kPa] for truncated pyramid dielectric structure. The simulation-based analysis indicates that the microstructured dielectric can enhance large deformation and effectively improve the device’s sensitivity.
Cross-Corpus Speech Emotion Recognition Based on Few-Shot Learning and Domain Adaptation

Within a single speech emotion corpus, deep neural networks have shown decent performance in speech emotion recognition. However, the performance of the emotion recognition based on data-driven learning methods degrades significantly for the cross-corpus scenario. To relieve this issue without any labeled samples from the target domain, we propose a cross-corpus speech emotion recognition based on few-shot learning and unsupervised domain adaptation, which is trained to learn the class (emotion) similarity from the source domain samples adapted to the target domain. In addition, we utilize multiple corpora in training to enhance the robustness of the emotion recognition to the unseen samples. Experiments on emotional speech corpora with three different languages showed that the proposed method outperformed other approaches.
In vitro antibacterial screening of methanolic extract of whole body tissue and ethylene diamine tetra acetate (EDTA) extract of cuttlebone of Sepia pharaonis (Ehrenberg, 1831) against selected clinical isolates

The need for the discovery of new and novel antibiotics is imperative because evidence suggests that development and spread of resistance to any new antimicrobial agent is inevitable. In the present study, the in vitro antibacterial activity of methanolic extract of whole body tissue and ethylene diamine tetra acetate (EDTA) extract of cuttlebone (polysaccharide) of Sepia pharaonis was investigated against ten bacterial species including Gram-positive species (Staphylococcus aureus and Streptococcus pyogenes) and Gram-negative species (Salmonella typhi, Klebsiella pneumoniae, Vibrio cholerae, Klebsiella oxytoca, Escherichia coli, Salmonella paratyphi, Vibrio parahaemolyticus and Proteus mirabilis) with different concentrations such as 25, 50, 75 and 100% using disc diffusion method. The highest inhibition zone was recorded against P. mirabilis for methanolic extract (18.3±0.1 for 100% concentration) and against S. pyogenes for EDTA extract (polysaccharide) (15.5±0.06 for 100% concentration) of cuttlebone. But the activity was totally absent in negative control. For minimum inhibitory concentration (MIC) technique, various ranges of concentrations between 20 and 100 mg/ml were prepared and tested. MIC values were found ranging from 40 and 100 mg/ml. All assays were carried out in triplets. A wide spectral and concentration dependent antibacterial activity was recorded in both extracts. Key words: Antibacterial activity, polysaccharide, cuttlebone, methanolic extract, ethylene diamine tetra acetate (EDTA), minimum inhibitory concentration (MIC).
Automatic Test Pattern Generation of Multiple stuck-at faults using Test Patterns of Single stuck-at faults

The fabricated circuitries are getting massive and denser with every passing year due to which a normal automatic test pattern generation technique to detect only the single stuck-at faults will overlook the multiple stuck-at faults. But generating test patterns that can detect all possible multiple stuck-at fault is practically not possible. Hence, this paper proposes a method, where multiple faults can be detected by using test vectors for detecting single stuck-at faults. Here, the patterns for detecting single faults are generated and their ability to detect multiple stuck-at faults is also analyzed. From the experimental results it was observed that, the generated vectors for single faults cover maximum number of the multiple faults and then new test vectors are generated for the undetermined faults. The generated vectors are optimized for the compact test patterns in order to reduce the test power.
Bias Reduced Semidefinite Relaxation Method for 3-D Rigid Body Localization Using AOA

This paper addresses the angle-of-arrival (AOA) based rigid body localization problem, where the position and orientation of the rigid body are estimated. Improving the robustness and reducing the estimation bias are crucial in AOA localization when operating in high noise environments. We develop a new semidefinite relaxation (SDR) method for this localization problem, with the additional novelty of having bias reduction. We begin by transforming the AOA measurement model, and then use it to formulate a constrained weighted least squares (CWLS) minimization problem with the rotation matrix and position vector as the optimization variables. Bias reduction is accomplished by introducing an auxiliary variable and imposing one quadratic constraint. The constraints on rotation matrix make the CWLS problem non-convex and difficult to handle. We relax the CWLS problem as a convex semidefinite program (SDP) by performing SDR, with second-order cone constraints added to tighten the relaxed SDP problem. The resulting tightened SDP has the ability of reaching a rank-1 solution with considerable small bias. Moreover, we provide a new technique in conducting the performance analysis. We show by mean square error (MSE) analysis that the solution performance is able to approach the Cramer-Rao lower bound (CRLB), and also derive the theoretical expression of the estimation bias. The one-anchor case is treated separately and a different SDP problem is derived for achieving good performance. Simulation results validate that the proposed estimator has a much lower bias than the existing solution and maintains the MSE approaching the CRLB at higher noise level.
Efficient FPGA Implementation of a Convolutional Neural Network for Radar Signal Processing

Although neural networks, especially convolutional neural networks (CNNs), have been successfully applied to many domains, there have not found many radar applications mainly due to a paucity of available training data. Focusing on fixed-site radars, this work uses in-situ collected data to train a CNN classifier and suppress clutter components that allow targets to “hide in plain sight. ” This paper describes a software and hardware co-design approach for implementing a neural network to improve radar signal processing. At the algorithm level, we propose using the ResNet10 model structure and other optimizations trained using the angle-Doppler spectrum of returns at each range. The FPGA implementation is then carefully optimized to better tradeoff performance and energy efficiency. Experimental results show our approach achieves better performance than conventional methods and exceed the requirement by more than $2.5 \times$. Meanwhile our energy consumption is much lower than other platforms like GPU. Our optimization methods can be applied to other CNN structures for efficiency improvement.
Design of H∞-based Robust Controller for Single-phase Grid-feeding Voltage Source Inverters

This article proposes the design of H∞ -based robust current controller for single-phase grid-feeding voltage source inverter with an LCL filter. The main objective of the proposed controller is to have good reference tracking, disturbance rejection and sufficient LCL resonance damping under a large range of variations of grid impedance. Based on the aforementioned performance requirements, frequency dependent weighting functions are designed. Subsequently, the sub-optimal control problem is formulated and solved to determine the stabilizing controller. Computational footprint of the controller is addressed for ease-of-implementability on low-cost controller boards. Finally controller hardware-in-the-loop simulations on OPAL-RT are performed in the validation stage to obtain performance guarantees of the controller. The proposed controller exhibits fast response during transients and superior reference tracking, disturbance rejection at steady-state when compared with proportional- and resonant-based current controllers.
Influence of the Particle Size of Glass Powder on Sintering Characteristics in TGV Packaging

In the fabrication of TGV (through glass via) reflow process, nano-glass powder is used instead of glass substrate, and the structure is filled first and then heated to reflow, which can simplify the process steps and improve the filling effect. This paper represents the preparation of nano glass powder for TGV, and by studying the sintering characteristics of glass powders of different sizes at different temperatures, mixing particles before sintering can accelerate the melting of larger particles, reduce the requirements for grinding process, improve bonding strength and save costs. The silicon structure is filled with the mixed powder and thermally reflowed. The reflowed glass powder can completely fill the edges of the structure and is tightly combined with the silicon.
Recognizing Plant species using Digitized leaves- A comparative study

Plants play a crucial role in nature and the well-being of the population. They have a significant contribution towards ecological stability and are also sources of our needs like food, medicine, and essential commercial products. As a result of massive scale deforestation, topsoil erosion, and habitat destruction, both the number and type of plants' existing species are steadily declining. So, plantation and identification and classification of plant species are essential for preserving plant species and accelerated farm as it will help in the better understanding of plants. Nevertheless, they are difficult to exercise as plant identification needs domain knowledge and experience. However, due to advances in machine learning and deep learning, this problem is tackled correctly. Various machine Learning and Deep Learning algorithms like Support Vector Machine, Artificial Neural Network, Convolutional Neural Network, Probabilistic Neural Network have successfully experimented on plant leaf images to identify the species with near correct accuracy. This article attempts a comparative analysis of various approaches used for plant identification. Several experiments with Swedish leaves confirm the effectiveness of machine learning and CNN based classification model.
Analysis of Static Noise Margin of 10T SRAM Using Sleepy Stack Transistor Approach

The latest electronic gadgets demand many functionalities which requires enhanced performance of the processor. To ensure this, cache based on Static Random Access Memory (SRAM) is a vital part in electronic devices. Above 90 nm technology, we can build an SRAM in CMOS LSI technology without electrical stability, by connecting six transistors. However, leakage and variability of transistors in SRAM cell have become dominating factor below 90nm technology. So we must design SRAM bit cell with utmost care. Recently SRAM cells have been designed to deal with these problems. However, it is an important task to achieve a balanced performance between all SRAM cell parameters of sub-nanometer technology. A new design of SRAM is presented in this paper to elevate the performance. The proposed SRAM is designed and implemented in CMOS 90nm technology and produces comparatively better performance in terms of Static Noise Margin (SNM), stability and power dissipation when compared with conventional 6T SRAM. The cell reduces the energy consumption by using the technique of stacking. The stability of proposed SRAM is also high compared to recent designs.