Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Zinc Oxide Nanoparticles Biosynthesized by Leuconostoc Mesenteroides ssp. Dextranicum Against Bacterial Skin Infections

The current study included a unusual method for the biosynthesis of ZnO nanoparticles using Iraqi isolate a probiotic bacteria Leuconostoc mesenteroides ssp. dextranicum. The characterizations of ZnO nanoparticles were performed by Xray diffraction (XRD) Atomic Force Microscopy (AFM) technique, Scanning Electron Microscopic (SEM), Fourier Transform Infrared Spectroscopy (FTIR) and Energy-dispersive X-ray analysis (EDX) spectra. The results confirmed that the obtained ZnO nanoparticles are well dispersed, stable and spherical with maximum particles in size range within 37–41 nm in diameter. The aim of this study was to determine the antibacterial and antivirulence factors properties of biosynthesized ZnO nanoparticles against bacterial skin infections including Acinetobacter baumanii ,Klebsiella pneumonia, Pseudomonas aeruginosa and Staphylococcus aureus. The antibacterial activity was evaluated through the determination of the minimum inhibitory concentration (MIC), the results obtained suggested that the MIC ranged between (25 -50) mg/mL and the anti-virulence factors also determined against hemolysin, urease , pyocyanin production and biofilm formation of tested bacteria. Results showed that the ability of hemolysin, urease and pyocyanin production were decreased after treatment with biosynthesized ZnO nanoparticles, also the best effect has been shown in biofilm formation after 72 h for all isolates, with high inhibition 48.74% against Staphylococcus aureus (S9) .
A Blended Improved H5 Topology With ILQG Controller to Augment the Performance of Microgrid System for Grid-Connected Operations

The development of the world demands on the progress of electricity operation and quality enhancement. Microgrid (MG) is a solution that manages the additional need of electricity. The MG generates electricity mostly from renewable energy sources, e.g. solar, wind and hydro. Most renewable energy sources require energy storage system as they are highly unpredictable in nature. An inverter is needed to convert that stored DC power to AC. The presence of a capacitor within the grid provides a galvanic connection that varies the common mode (CM) voltage of the inverter. Thus, the leakage current as well as the total harmonic distortion (THD) of the grid voltage and current increases. The increase of the leakage current hampers the safe operation of the MG. Now-a-days transformerless inverter is gaining more attention because of high efficiency, low cost, less weight and the capability of eliminating the high frequency component from the inverter CM voltage. In addition, a controller inclusion with a transformerless inverter can maintain a near-seamless performance. This paper proposes a blended technique of an improved H5 topology with an Integral Linear Quadratic Gaussian (ILQG) controller for grid-connected MG operations, that reduces the inverter leakage current by keeping the CM voltage constant. Thus it has the capability to reduce the THD of voltage and current. A comparative analysis has been carried out between this technique and existing topologies with different modulation techniques that helps to find out the most optimum inverter package to eliminate the leakage current and reduce the THD of grid voltage and grid current to almost zero.
Compressing Deep Models using Multi Tensor Train Decomposition

Deep learning is a very influential research direction in data science and has been applied to various fields in recent years. However, deep neural network usually comes with high computational complexity and large memory storage requirement, thus is difficult to be deployed in mobile devices. The technique of deep neural network compression can reduce the parameter redundancy in depth models by using the low rank and sparsity characteristics of weight matrices or tensors and therefore suitable for applications in mobile systems. In this paper, we employ the Tensor Train decomposition with sparsity constraint for the compression of both convolutional layers and the fully connected layers. Furthermore, the idea of multi Tensor Train decomposition is proposed to improve the performance of the compression model. Specifically, a set of patterns are defined to reshape the original weight matrices or tensors into different shape of high dimensional tensors for compression using Tensor Train decomposition. New network structure is then built based on the decomposition and the number of parameters is highly decreased. Experiments show that the robustness of the compression model increases with the number of patterns employed in the deep model, and the proposed compression approach can achieve a high compression ratio with small performance loss.
TOPAZ Safety Assessment of Conflict Resolution for Unmanned Aerial Vehicles

Considering the lack of safety assessment for Unmanned Aerial Vehicles (UAV) operating in airspace, a safety assessment system based on TOPAZ method is constructed for UAV flight conflict resolution technology. Simulation shows that, through iterative process of TOPAZ, the safety bottleneck of UAV conflict resolution algorithm can be further found, and it is confirmed that the current various types of conflict resolution technology can not satisfy the safety requirements of manned aircraft operating in airspace. This can provide a theoretical reference for the construction of future UAV airspace operation safety assessment system.
Optimal Assignment Plan in Sliced Backhaul Networks

The 5G mobile network will rely on network slicing to provide a wide variety of services with various quality of service (QoS) requirements. Network slicing is promoted by 3GPP and provides a logical vertical partition of the network that is based on network virtualization technologies, namely, network function virtualization (NFV), software-defined networking (SDN) and ETSI multi-access edge computing (MEC). Despite the undisputed benefits in terms of flexibility and scalability that are pledged by the paradigm, network slicing requires intelligent resource scheduling and allocation algorithms to efficiently use the network resources, especially at the edge of the network, due to their scarcity. In this paper, we propose an optimization algorithm for steering data traffic of multiple slices in the edge backhaul network, which aims at maximizing the QoS.We extensively analyze the realizable grade of QoS by testing various levels of MEC resources, demonstrate the beneficial impact of the approach for mobile operators, and highlight the performance advantage that is realized versus a single-slice approach of undifferentiated traffic.
Bidirectional Converters Topologies, Control Techniques and Switching Strategies: An Overview

The different topologies of the DC/DC conversion is reviewed in the paper, control techniques and switching strategies for the charging stations and the electric vehicles of hybrid nature with the pul in facilities. The review of these converters is presented, compared and analyzed based on switching frequency, effectiveness, component count, output power, and cost. The bidirectional converter works in both the direction between G to V that is the grid to vehicle as well as the vehicle to grid which is termed as V to G utilizing different schemes of switiching and strategies with the control performed using the phase shift schemes Therefore the classification and basic knowledge of DC-DC convertors that rely on isolation as well as non-isolation and phase-shifted controlling techniques and zero-current (ZC) and zero-voltage (ZV) soft-switching and hard switching schemes are also in this paper.
Enhancing Performance Aspect in Usability Guidelines for Mobile Web Application

The rapid growth of technology has boosted the usage of mobile devices to access a web application. These devices have various specifications such as screen sizes and resolutions. Responsive Web Design (RWD) approach bridges the gap of these differences by adapting the flexibility concept in screen sizes and resolutions, thus ensuring better website usability. Poor performance in website usability resulting in declination in number of users. Therefore several existing Usability Guidelines (UG) were invented mainly for a desktop-based website and unfortunately lesser studies have been carried out for a Mobile Web Application (MWA) especially the performance aspect of it. Henceforth, this paper focuses on enhancing the performance element of an existing UG by analysing, identifying, proposing, implementing, and measuring the new additional performance attributes in UG for MWA. We used First Contentful Paint (FCP), Speed Index (SI), Time to Interactive (TtI), First Meaningful Paint (FMP), First CPU Idle (FCI) and Estimated Input Latency (EIL) to measure the performances of two case studies and the result shows better score at 90-100 (fast-GREEN) with the proposed performance attributes compared to another website without it which averages at 50-S9 (average-ORANGE) range.
A Semi-supervised Naive Bayesian Method for Labeling Heterogeneous Fingerprints

The traditional construction of radio maps for indoor localization suffers from extensive work loads. Labeling the fingerprints of passive crowdsourcing can effectively alleviate the work pressure of radio map construction while maintaining high location estimation accuracy. Although Naive Bayes method shows stable classification ability in presence of less correlated and missing data, which are the characters of RSSI data in WiFi positioning, the frequency histogram based native Bayes methods show poor performance in estimating conditional probability when the interval setting of discretization is not appropriate. So, we use KDE to calculate the conditional probability of naive Bayes instead of frequency histogram. Consider that the fingerprints to be labeled can provide valuable information, we further propose the transductive semi-supervised method where the fingerprints to be labeled assist in the learning process. The RMSE of experimental data is 2.418m, more accurate than other benchmark algorithms, proving the validity and efficiency of the proposed algorithm for labeling crowdsourced heterogeneous fingerprints.
Building Information Modeling: Factors Affecting the Adoption in the AEC Industry

The Architecture, Engineering and Construction (AEC) industry is the backbone of the national economy with a large contribution to the domestic revenue. There is a lack of adopting modern ways of managing building information that is a cause of low productivity in construction projects. BIM provides capabilities of cost reduction, increase quality, enhanced productivity and on-time delivery. However, the adoption and implementation of BIM are susceptible to confrontation experienced in the whole world. Therefore, the purpose of this study is to empirically examine the factors that influence BIM adoption in the AEC industry. A literature review is performed to find the factors and factors are categorized into four dimensions. A total of 74 factors are found and are categorized into Individual, organizational, Technological and Environmental dimensions. This research study will enhance researchers understanding of factors affecting BIM adoption.
A Review of Natural Language Processing Techniques for Sentiment Analysis using Pre-trained Models

Research and industry are becoming highly interested in automatically analyzing the opinion of general public from social networks with respect to a particular subject. Extracting the polarity from these data is always remaining as a significant bottleneck. Pre-trained models built on deep learning architecture can achieve this task in an effective manner by using transfer learning approach. Since it is difficult to develop a model from scratch, due to time constraints or computational limits, pre-trained models with vast potential and possibilities were introduced. They provide a benchmark to either improve the existing model or test the developed model against it. This paper discusses about various word embedding methods used for sentiment analysis followed by an overview on state-of-the-art pre-trained models used for natural language processing, which is commonly used in the process of sentiment analysis. Experimental results of two state-of-the-art pre-trained models are also analyzed.
Hybrid 2D-3D Fast Electromagnetic Analysis aided by Pattern Recognition for Signal Integrity Analysis

The use of 3D full-wave electromagnetic solvers for signal integrity analysis is often hindered by its prohibitive time and memory requirements. In this work, a hybrid 2D-3D solver is proposed to address the computational challenges while still preserving the desired accuracy. The method is based on an image based segmentation technique followed by 2D-3D classification for each segment, analysis and cascading transfer functions. A figure of merit that accurately represents simulation accuracy in frequency domain is identified for the 2D-3D prescription. Numerical results for a 13" trace is presented and the benefit in simulation time versus accuracy are highlighted.
A 0.5-V 2.07-fJ/b 497-F2 EE/CMOS Hybrid SRAM Physically Unclonable Function with < 1E-7 Bit Error Rate Achieved through Hot Carrier Injection Burn-in

This paper presents a bit-error free SRAM-based physically unclonable function (PUF) in 130-nm standard CMOS. The PUF has a compact bitcell, with a bitcell area of 497 F2. It switches from EE SRAM to CMOS SRAM mode during evaluation, achieving high native stability, low-voltage evaluation, and low-power operation. Its stability is reinforced to 100% through hot carrier injection (HCI) burn-in on the alternate-direction nMOS load, which causes no visible oxide damage and does not require additional fabrication processes or extra transistors in the bitcell. Experimental results show that the prototype chips achieved actual zero bit error across 0.5-0.7 V and -40°C to 120 °C, as well as zero error (<1E-7 BER) at the worst VT corner after accelerated aging test equivalent to ~21 years of operation. The PUF functions stably down to 0.5 V, with an energy of 2.07 fJ/b, which includes both the evaluation and read-out power. The secure, compact, low-power and 100% stable features of the PUF make it an excellent candidate for the resource-constrained Internet of Things security.
A Comparison between PID controller and ANN controller for speed control of DC Motor

This article describes the benefits of application the intelligent controls methods such as artificial neural network ANN to control the separately excited of DC motor and compared it with the traditional controllers. These modern neural network PID techniques improve the speed response of separately excited DC motor. Simulation results are obtainable to validate the influence and returns of using ANN with DC motor, also the dynamic behaviour parameters that obtained from ANN-controller are more acceptable as compared with PID-controller.
Neuromorphic Implementation of Spiking Relational Neural Network for Motor Control

Despite the rapid development of robotic control theory, hardware motor controllers still suffer from some disadvantages: they are computationally-intensive and rely on powerful computing systems which are usually implemented using bulky and power-hungry devices. On the other hand, biological motor control systems are power-efficient, light-weight and robust. Neuromorphic engineering sheds a light on how to uncover biological control features that could lead to the design of lower power and less bulky controllers. In this paper, we present a closed-loop motor controller implemented on mixed-signal analog-digital neuromorphic hardware using a spiking neural network. The network performs PI control by encoding target, feedback and error signals using population coding. It continuously calculates the error through the network, which relates the three variables by means of feed-forward inter-population synapses. This biologically plausible and fault-tolerant strategy is ideally suited for the use of neuromorphic hardware that comprises noisy silicon neurons. Here we show how to optimize the network structure to make it robust to both noisy inputs and device mismatch. We provide experimental results showing how the controller can reach 97.1% accuracy with 75.8 ms average latency.
Robust and Lightweight Mutual Authentication Scheme in Distributed Smart Environments

In the smart environments several smart devices are continuously working together to make individuals’ lives more comfortable. Few of the examples are smart homes, smart buildings, smart airports, etc. These environments consist of many resource constrained heterogeneous entities which are interconnected, controlled, monitored and analyzed through the Internet. One of the most challenging tasks in a distributed smart environment is how to provide robust security to the resource constraint Internet-enabled devices. However, an authentication can play a major role ensuring that only authorized devices are being connected to the distributed smart environment applications. In this paper, we present a robust and lightweight mutual-authentication scheme (RLMA) for protecting distributed smart environments from unauthorized abuses. The proposed scheme uses implicit certificates and enables mutual authentication and key agreement between the smart devices in a smart environment. The RLMA not only resists to various attacks but it also achieves efficiency by reducing the computation and communication complexities. Moreover, both security analysis and performance evaluation prove the effectiveness of RLMA as compared to the state of the art schemes.
Rapid Recognition of Human Behavior Based on Micro-Doppler Feature

In this paper, we use principal component analysis (PCA) and Mel frequency cepstrum coefficient (MFCC) to study the classification of human behavior based on micro-Doppler features. The PCA can reduce linear dimensionality and computational complexity. The MFCC can extract micro-Doppler features of different frequencies. The combination of PCA and MFCC can reduce the time which is used to extract the micro-Doppler features of human behavior and the time which is used to calculate the feature extraction. So, the combination method can realize real-time processing. We collect data from 6 differential behaviors of 12 person by using the frequency modulated continuous wave (FMCW) radar. And then, we classify human activities by using support vector machine which is extracted from PCA and the MFCC feature vectors. Experimental results show that our method has a good recognition rate.
Three-Dimensional Mapping System for Environment and Object Detection on an Unmanned Aerial Vehicle

This study aims to solve the problem of multiple devices and methods used in surveying as well as a low-cost alternative to 3D mapping using a LiDAR; which would be used for surveying post-disaster areas. To solve this problem, a system composed of 2D stitching, object detection, 3D mapping, and LiDAR point cloud data processing is created. The study includes utilizing a camera-equipped unmanned aerial vehicle for data gathering of the system.
Comparison of Machine Learning Techniques for Predictive Modeling of High-Speed Links

We compare three different machine learning techniques for constructing predictive model for eye opening based on channel length and interconnect cross-sectional geometry. Surrogate model is constructed using sparse grids, support vector regression, and artificial neural networks. Models for training data are generated using quasi-TEM modeling of the interconnect, and eye opening training data is obtained from statistical high-speed link simulation using IBIS-AMI transmitter and receiver models. Numerical results illustrate that all three methods offer reasonable predictions of eye height, eye width and eye width at 10−12 bit error rate.
A 624μW Variable Gain Amplifier with 45dB peak Gain using Current Controlled Degeneration

This paper presents a current controlled variable gain amplifier (VGA) with a two-stage current-reuse gain cell and a tail current source for controlling the current. VGA is aiming to offer high voltage gain, low Noise Figure (NF) while operating with low-power consumption for high-performance wireless applications. The low-voltage low-power VGA is designed and implemented in UMC 180nm CMOS technology using Cadence virtuoso design tool. The proposed VGA can provide a maximum gain of 45.1dB while achieving a maximum IIP3 of 4.643dBm and minimum NF of 4.976dB. Power consumption of the VGA at maximum gain is 624. 4µW from 1V supply.
Speaker identification using convolutional neural network for clean and noisy speech samples

Conventional speaker identification systems require features that are carefully designed to achieve high identification accuracy rates. With deep learning, these features are learned rather than specifically designed. The improvements of deep neural networks algorithms and techniques lead to an increase in using deep neural networks for speaker identification systems in favour of the conventional systems. In this paper, we use a convolutional neural network with Mel-spectrogram as an input for the identification purpose. The experiments are done on TIMIT dataset to evaluate the proposed CNN architecture and to compare with state-of-the-art systems for clean and noisy speech samples.
Speech Automated Examination for Visually Impaired Students

A huge opportunity for scribe automation is to design a system specifically to support visually impaired and blind students in the process of achieving high levels of independence while attending examinations, by automating their exam-taking process. The ability of students to control and independently answer questions through voice helps reduce their reliance on human assistance. The prime goal of the proposed application is to solve the problem of students being dependent on scribes to take examinations. Upon clicking particular keys on the keyboard, questions will be dictated by using the concept of speech synthesis, and answers can be given accordingly based on the concept of speech recognition. Hence, not only visually impaired students can benefit from this but it will prove to be useful for the other handicapped people as well.
Cycle Condition Identification of Loader Based on Optimized KNN Algorithm

The working conditions of loaders alternate between stages of full or empty loads, loading or unloading, and moving forward or backward, which complicates the vehicle’s characteristic response. Based on the K-nearest neighbor (KNN) algorithm and a principal component analysis (PCA) method, stages recognition algorithm under the V-type working conditions of a loader was studied. First, the collected transmission signals were noise-reduced and filtered. Second, the PCA was used to reduce the dimensions of the data. Finally, the working condition samples were established from the data obtained, which were later trained and classified using the KNN algorithm. Compared with the neural network algorithm, the accuracy of the optimized KNN algorithm reaches 99.4%, and its running time is 2.1s. The algorithm described in this paper guarantees a high accuracy under recognition of the loader conditions in a short running time. It can be extended to an intelligent identification using big data and artificial intelligence control of the construction machinery.