Miracle-dz/UniFAR-FTUs huggingface dataset:
In Silico Screening and Testing of FDA-Approved Small Molecules to Block SARS-CoV-2 Entry to the Host Cell by Inhibiting Spike Protein Cleavage

The COVID-19 pandemic began in 2019, but it is still active. The development of an effective vaccine reduced the number of deaths; however, a treatment is still needed. Here, we aimed to inhibit viral entry to the host cell by inhibiting Spike (S) protein cleavage by several proteases. We develop a computational pipeline to repurpose FDA-approved drugs to inhibit protease activity and thus prevent S protein cleavage. We tested some of our drug candidates and demonstrated a decrease in protease activity. We believe our pipeline will be beneficial in identifying a drug regimen for COVID-19 patients.
Development of an optimal impact energy absorber for highway crash cushions

Development of an Optimal Impact Energy Absorber for Highway Crash Cushions. (August 2005) Christopher Ryan Michalec, B.S., Texas A&M University Chair of Advisory Committee: Dr. Steve Suh The objective of this research is to develop a new and efficient method of absorbing a vehicle’s kinetic energy for highway safety crash cushions. A vehicle that makes a direct impact with a rigid highway structure traveling at highway speeds can be fatal for its occupants. Crash cushions are implemented on roadways in front of these rigid structures with the intent to “soften” the impact. The cushion will bring a vehicle to a stop at safe rates before it impacts the rigid structure. The energy absorbing component of the crash cushion must meet four main requirements. The cushion must reduce the vehicles speed at a rate that does not allow the occupant to impact the vehicle interior at velocities greater than 12 m/s. The cushion must then bring the vehicle to a complete stop with deceleration rates below 20 g’s. A crash cushion must satisfy these requirements for an 820 kg vehicle and a 2000 kg vehicle traveling at 100 km/hr. Advanced design methodologies were applied to enable multiple, innovative design concepts. These concepts made use of the deformation of steel in structural pipe, structural angle, and structural plate to reduce the velocity of a vehicle at a safe rate. Critical design parameters were identified which allowed for efficient and effective numerical experiments to be conducted. The data collected from these experiments were then validated when compared to physical test data. After the data had been collected, each of the designs was compared to one another in order to decide upon the best design. The design selected was the deforming plate concept which makes use of steel plate mounted in a fashion that created two arms that acted similar to two cantilever beams. A wedge was forced beneath these arms deforming them upward. This
Collision-Free Dynamic Convergecast in Low-Duty-Cycle Wireless Sensor Networks

Convergecast is a fundamental operation in wireless sensor networks (WSNs). To support long-term deployment of WSNs, sensor nodes normally operate at low-duty-cycles. However, the low-duty-cycle operation significantly reduces the communication chance between nodes. Consequently, the risk of data collisions significantly increases when multiple senders transmit packets to a receiver during its very short active period. This problem further causes not only wasted packet retransmissions, but also a large delivery latency. Under such conditions, collision-free medium access is more appealing than recovering after collision for low-duty-cycle WSNs. In this work, we propose an incast-collision-free convergecast protocol, named iCore, to address the many-to-one collision problem in low-duty-cycle WSNs. iCore employs the dynamic forwarding technique, establishes a non-conflicting schedule for efficient convergecast, and improves the channel utilization by allowing senders to opportunistically transmit packets once detecting unused slots. Specifically, we design efficient forwarder assignment and forwarding optimization algorithms that ensure low end-to-end latency under diverse data traffic types. Through comprehensive performance evaluations, we demonstrate that, compared with the baseline protocol, iCore effectively minimizes the end-to-end delay by 25% ~ 57% and maintains high delivery ratio and energy efficiency for different many-to-one convergecast scenarios.
Visibility Restoration of Hyperspectral Images

In the applications including aerial and space imagery the use of hyperpsectral images came into existence that includes detection of targets, agricultural, tracking as well as the natural resource exploration. Unfortunately, with the secondary illumination, atmospheric scattering, sensor noise, changing viewing angles the quality of the images is degraded. Enhancing the images has a high pay off since the count of spectral bands is in hundreds that increases a load of computation excessively. Moreover considering the bands separately fails to make use of information present across them. The visibility restoration of the hyperspectral images is a highly desired technology in the applications of computer vision. The situation is also a perplexing task since the visibility of the numerous bands varies and hence are different in the visibility offered by them. To enhance the visibility the consensual purifying is used. The grayscale and color images are produced using the consensual purifying wherein the abstracted image is generated. Then the Gabor purifying is used followed by the use of Gaussian function. The technique proposed is well appropriate for solicitations where each local, as well as comprehensive correlation, exist among the supplementary image beside the enhanced image. Experimental results with HSI data derived from Salinas are demonstrated that are obtainable demonstrating the effectiveness of the projected estimator.
Implementation and Performance Analysis of 2:1 Multiplexer using Different Logic Families at 130nm Technology

Multiplexer (MUX) which is also familiar as an input or information selector and a device that opts joining different analog or digital signal inputs and ahead the marked put in to sole yield. It is first of the salient components which upgrade the effectiveness of data transversal. Multiplexers are used to expand the amount of the data that can transferred over a line of network with given bandwidth over a time of period. There are different multiplexers for digital and analog signals. A digital multiplexer has digital input signals coming from different data-acquisition networks. In this project, a 2:1 multiplexer is analyzed by numerous CMOS logics which are Pseudo NMOS logic, Static CMOS logic, Dual-Rail Domino logic and Domino logic which recognize the foremost families that is appropriate for the outline of greatest methods of multiplexer. The execution is implemented in VLSI automation because it have characteristics such as less size, less cost, great operating speed and less power. The accomplishment research of the multiplexer using different logic families are executed in DSCH, Micro wind and Mentor graphics at 130nm.The results gathered shows that the Domino logic method based 2:1 multiplexer is the well-organized method. The Domino logic outshines the different logics that the work recommends that the giant elevated different level multiplexer with shallow power and power delay product can be accomplished using method of Domino logic.
ESD HBM Discharge Model in RF GaN-on-Si (MIS)HEMTs

We report on our extensive experimental and simulation study to understand ESD failure mechanisms in RF GaN-on-Si (MIS)HEMTs. As opposed to ESD clamp transistors in LV CMOS technologies, a mis-correlation between standard-defined HBM ESD robustness and commonly used TLP failure current was observed in GaN (MIS)HEMTs. Using transient HBM IV characteristics, a novel discharge model is proposed to explain the transient discharge mechanism. The TCAD and SPICE simulations confirmed that the observed mis-correlation between TLP and HBM is attributed to 2DEG channel resistance modulation in response to HBM ESD transient voltage waveforms.
Voice Feature Analysis For Speaker Recognition To Improve Content Management

We are introducing feature extraction approach which helps in content control on broadband television. Speaker recognition-based content control are more effective as compare to traditional permission-based method. Television content can be controlled based on the program requester’s age and program requester’s age can be identified based on his/her voice. Speaker is adult or not that can be identified based on his/her voice. Every person’s voice is unique based on voice features like pitch, frequency, jitter, amplitude, and spectral power. This article explains how we can effectively use these features to classify child and adult voice. In general, Mel-Frequency Cepstral Coefficient (MFCC) is used for effective feature extraction and it provides features’ numerical values. Artificial Intelligence (AI) based system uses these features for accurate speaker recognition. Child and adult voice can be differentiate at time, frequency, mel scale level, which can help for clear classification of the speaker. Power spectrum and mel-spectrum also represent difference in child and adult voice.
A Hybrid Antenna Structure for Cognitive Radio Applications

Due to rapid growth of 5G communication and mobile systems, there is a huge requirement of wideband, multiband and UWB antenna. Considerable research is going on to create new appropriate solutions to satisfy the challenging constraints of 5G wireless communication systems. In this paper we optimize the antenna size to reach essential requirements for spectrum sensing and communication. In this article a reconfigurable antenna and a UWB antenna is built on the single side of the substrate. Even though both UWB monopole and narrow band antennas are integrated together but they are excited by different ports and work independently. So, the final combined antenna detects and communicate over a wide frequency range from 3GHz to 13GHz.
A synchronization position control method based on dynamic particle swarm optimization algorithm in electro-hydraulic servo system

For solving dynamic position error problem and improving synchronization position control precision in electrohydraulic servo system, a dynamic particle swarm optimization algorithm is established by means of the introducing of moving windows and forgetting factor in searching regional, and an electro-hydraulic servo system synchronization position control method is presented by dynamic particle swarm optimization algorithm. Finally, this method was demonstrated and validated by a sample. It has shown the proposed method can improve the benefit of position control precision in electro-hydraulic servo system synchronization control, and provide a support for adapting to complex and dynamic synchronization control in electro-hydraulic synchronization position servo system (EHSPSS).
A Novel Android Malware Detection Method Based on Visible User Interface

Machine learning has been increasingly adopted to detect Android malwares. Most existing studies depend on features in code space such as information flows and API calls. Malware variants would engage these models in a never-ending war. Inspired by the observation that some variants share similar or even identical user interfaces (UIs), this paper explores employing visible UI screenshot as the indicator to build a novel Android malware detection method. To achieve this vision, we built the first Android Application Screenshot Dataset (AnASD) consisting of more than twenty thousand UI screenshots produced by both benign applications and malwares. A thorough analysis was conducted to characterize the dataset, especially the UI difference between benign applications and malwares. Then a set of state of the art deep learning classifiers on AnASD were trained and evaluated. The results of both sim-ilarity measurement and classification performance proved the feasibility to detect Android malwares based on user interfaces. To facilitate the research community, the dataset is free available at https://doi.org/10.6084/m9.figshare. 14445768.
Design of Electronically Steerable Direction Shifting Microstrip Antenna Array using Beam Steering Technique

A direction shifting electronically beam steerable Microstrip antenna array is proposed. In order to reduce the overall size, the antenna array is designed on a dielectric substrate. A single antenna element in the array consists of a dipole element and two parasitic elements, designed over a multilayer dielectric structure. The length of the parasitic elements can be reconfigured using RF switches. Switching the length of the parasitic elements in various configurations, the main lobe of the single antenna of the array is shifted in three directions. This leads to three modes of operation. By configuring the switches of every antenna in the array, the main lobe of the array shifts the direction. The main lobe of the antenna array in a particular mode is made steerable by changing the phase shift between the antenna elements. Simultaneously altering the length of parasitic elements by switches and changing the phase shift between the antenna elements in the array, the main lobe is made steerable in any of the three modes. This paper discusses the design of an array with pattern reconfigurability and beam steering on multilayer structure with a minimum number of RF switches. The proposed antenna is designed to operate in C Band.
Design of L-Shaped Strip Loaded Dual Band Hexagonal Shaped Circularly Polarized Monopole Antenna

A hexagonal-shaped circularly polarized monopole antenna operating in S-band and C- band is designed in this paper. It consists of two symmetrical L-shaped strips, a hexagonal-shaped radiator and a rectangular stub added in the CPW ground plane to achieve the dual wideband performance. Hexagonal shaped radiator provides large electrical length as compare to a circular or rectangular shape of the antenna, which provides resonance at a lower frequency. The proposed antenna design provides the dual-band that has impedance bandwidth (IBW) ranging from 80% (1.4-3.3 GHz) and 43.90% (4.8-7.5GHz), respectively, and the 3-dB ARBW for two bands are 52.17% (1.7-2.9 GHz) and 25.21 % (5.2-6.7 GHz), respectively. The proposed CP antenna is providing a maximum gain of about 3.5 dBi and 5.9 dBi in dual bands, respectively, with RHCP radiations. The volume of the proposed CP antenna is 40 × 34 × 1 mm3.
Internet of Things (IoT) Protocols, Communication Technologies, and Services in Industry

Emerging digital technologies have a great influence on industries with the revolution Internet of Things (IoT) that rapidly automates tasks and allows the inanimate physical objects to act without any human intervention. IoT promises to enhance humans’ quality of life by connecting smart devices, allowing them to make smart decisions independently. The Industrial IoT (IIoT) or Industry 4.0 is growing exponentially which revolutionizes the operations of the industries. Currently, every manufacturer can adopt Industry 4.0, the effect of which makes IIoT industries more productive. In this paper, we have discussed the IoT protocols and communication technologies and the trending services of IoT in industries. In this paper, we prompt attention to the need to discuss IoT with the advancements in industry 4.0 that is followed by a survey on IoT data protocols, communication protocols. We also discuss the various trends related to this revolution industry including big data, artificial intelligence, cloud computing, and cyber-physical systems. Finally, the key points or the future enhancement of IoT are discussed.
Deep-layer clustering to identify permission usage patterns of Android app categories

With the increasing usage of smartphones in banks, medical services and m-commerce, and the uploading of applications from unofficial sources, security has become a major concern for smartphone users. Malicious apps can steal passwords, leak details, and generally cause havoc with users’ accounts. Current anti-virus programs rely on static signatures that need to be changed periodically and cannot identify zero-day malware. The Android permission system is the central security mechanism that regulates the execution of application tasks. Although recent advances in research have provided various approaches and detection methods for finding malware apps, the available literature lacks a full analysis of this subject. We fill this gap by: 1) Systematically and automatically building a large dataset of malware and benign apps, which we have made available to the community. Our dataset has around 16K apps and 118 features. 2) We offer a novel approach for automatically identifying permission usage patterns, which are groupings of permissions that developers frequently utilise together. The approach combines SOM and K-means clustering algorithms to classify permissions according to app usage categories. The results demonstrate that the proposed methodology is able to detect most of the consistent and coherent permission usage patterns across a wide variety of application categories. To assess our strategy, we add the identified patterns as features to our dataset and then apply an SVM classifier for malware detection. Our results indicate that the identified patterns improve the performance of the classifier.
Defining and Identifying the Legal Culpability of Side Effects using Causal Graphs

Deployed algorithms can cause certain negative side effects on the world in pursuit of their objective. It is important to define precisely what an algorithmic side-effect is in a way which is compatible with the wider folk concept to avoid future misunderstandings and to aid analysis in the event of harm being caused. This article argues that current treatments of side-effects in AI research are often not sufficiently precise. By considering the medical idea of side effect, this article will argue that the concept of algorithm side effect can only exist once the intent or purpose of the algorithm is known and the relevant causal mechanisms are understood and mapped. It presents a method to apply widely accepted legal concepts (The Model Penal Code or MPC) along with causal reasoning to identify side effects and then determine their associated culpability.
Segmentation on Brain Tumor MRI Images Using Convolution Neural Network (CNN)

The Convolution Neural Network (CNN) splits the tumor into little 3x3 pieces, which is how the brain tumor is divided. Because of the lesser mix of masses within the association, tiny components allow for the consideration of a bigger arrangement, as well as having a favorable effect on over fitting. In this study, we proposed an approach that takes advantage of important information by employing a Convolution Neural Network (FCNN). Individual picture fixes and trims were used to prepare our network. In comparison to previous high-level techniques, our method may accomplish cutting-edge execution with a full learning pipeline that learns from the beginning to the end. the highlights in cycles for improved execution on the platform given undertaking.
An App to Assist Differently Abled People

The system proposes an application for differently abled people such as blind, deaf, dumb and hand disabled to communicate among themselves using Android Development. It includes voice enabled object detection and distance estimation assistance to blind using Tensorflow Lite Object Detection API, image to speech conversion using OCR text recognition with OpenCV and TextToSpeech API to assist the blind. It also provides navigation feature for blind using Mapbox and Geocoder library. The application also has text- to-speech conversion using TextToSpeech API and speech-to- text conversion using a Speech Recognition library to facilitate easy chatting among themselves. It also provides chatting option built using Firebase.
Road Extraction from Remote Sensing Imagery: A Survey

Roads play a vital role in daily-life and commercial activities and holds great importance for civilization establishment, transportation, economy etc. Remote sensing is developing more efficient methods for observation and collection of data, which caters to high demand of good quality data which can be used by ever-improving computer vision techniques for producing results with utmost accuracy. The need of faster and accurate extraction road segments with from aerial or satellite imagery is in high demands because of its vast application. This paper discusses in detail about basics of remote sensing, details about road extraction and previous works done in this area, the available road datasets, the existing methodologies and existing challenges in this field which will definitely help future researchers for understanding the area and obtaining a promising research direction.
Predicting Depression from Social Networking Data using Machine Learning Techniques

In this era of COVID-19 pandemic, as more people self-isolate themselves, psychological health issues like depression, anxiety, and stress is an increasing concern all over the world. The purpose of this study is to investigate the data from social forums, where we found communities of depressed people sharing their thoughts and emotions in the forums, these forums also receive advices and support. In this paper, we will analyse the "depressed" text; by manipulating the data, extracting features, categorising, and try to understand what are the attributes of "depressed" text, and how we can "predict" whether a text should be marked as depressed or not. Using text analysis and text data mining techniques, the text obtained from the social forums was analysed and three different machine learning algorithms were used to predict depression. After cross validation overall accuracy of 99.69% was obtained as the best score using the proposed system. This study definitively answers the question regarding using human basic language and communication of personal experiences, for the prediction of depression and can be reached easily. Furthermore, not only actions, habits and behaviour of a person, text too can be used for accurate diagnosis of depression.
Deep Learning-based Techniques for the Automatic Classification of Fundus Images: A Comparative Study

In fundus pictures, convolutional neural networks (CNN) have excelled. The majority of prior research has concentrated on recognizing a specific fundus illness. A feature extraction network is the first portion, and a multi- label classification neural network is the second. This work reviews various papers on the transfer learning technique for fundus classification. Transfer learning is used in this algorithm. Labeled picture samples are used to train the deep learning (DL) algorithm. The pre-trained DL model uses a transfer learning approach to add fresh pictures to the modal. The output probabilities of several models are combined to provide the final recognition result. As a result, the DL model can be trained on a smaller number of data sets while still producing good outcomes. Several classification techniques had been addressed in the literature for the classification of fundus images automatically in which deep learning techniques outperformed. In this paper different deep learning (DL) techniques for automatic classification of fundus images had been discussed and results are compared on the basis of accuracy, f1-score and AUC.
Design of an Efficient 4-Bit Serial Parallel Multiplier using QCA

This report provides an effective multi-layer serial-parallel 4-bit multiplier (SPM) using QCA technology. Using a completely used majority gate (MV) and a revised compact and flexible exclusive-OR (C-XOR) gate, we built a bit-serial full adder (BSA). The suggested C-XOR gate has been realized using the QCA cell's properties. For 4-bit multiplier, this updated circuit corresponds to a 38 percent and 26 percent reduction in number of QCA cells and occupied area analogized to the previous design. With QCADesigner, all the proposed layouts were simulated and tested.