Miracle-dz/UniFAR-FTUs huggingface dataset:
Speed Limit Signboard Recognition and Audio Alert for Driver Using CNN

This paper deals with designing and implementing an android application for alerting the vehicle driver with the voice alert command. The implementation begins with the speed limit sign Board detection and then the detected image is preprocessed after that CNN has applied in order the extract the features from the detected image and classify the image into one of the 8 classes after the image is classified a voice alert command will be given to the driver using the mobile speaker. The dataset is downloaded from the Kaggle German Traffic Sign detection to train the model, which is a benchmark dataset.
A Novel Framework for Ground Segmentation Using 3D Point Cloud

Ground segmentation is an essential preprocessing task for autonomous driving. Most existing 3D LiDAR-based ground segmentation methods segment the ground by fitting a ground model. However, these methods may fail to achieve ground segmentation in some challenging terrains, such as slope roads. In this paper, a novel framework is proposed to improve the performance of these methods. First, vertical points in the point cloud are filtered out by a gradient-based method. Second, a polar grid map is built to extract the seed points for model fitting. Moreover, the fitting-based method is used to model the ground. And a coarse segmentation result can be obtained by the fitted model. Next, the coarse segmentation result is used to update the ground height value for each grid in the grid map. Finally, the segmentation result is refined by the grid map. Experiments on the SemanticKITTI dataset have shown that the fitting-based method can achieve more accurate segmentation results by integrating with our proposed framework.
Survey On Smart Agriculture Using Iot

Each day, the agriculture sector in India loses ground on both sides, which reduces the ecosystem’s potential for production. To resuscitate agriculture and return it to a path of higher growth, a solution is increasingly needed. It takes a lot of maintenance, expertise, and management to maintain a large-scale agricultural system. The Internet of Things (IoT) is a system of networked devices that enables both automated job fulfilment and data transmission and reception through the internet. The crop yields are boosted by the abundance of data analysis parameters offered by the agricultural sector. Information and communication are modernized through the usage of IoT devices in smart farming. Soil moisture, minerals, light, and other elements can be expected for better crop growth. This study examines a handful of these traits for data analysis in an effort to help consumers use IoT to make better agricultural decisions in farming and irrigation. The method is designed to assist farmers in boosting agricultural productivity.
Stacked Bidirectional-LSTM Network for FakeNews Detection on Twitter Data

This work suggests a computational approach incorporating deep-learning methods to combat false information. The goal is to raise the false information detection scores by utilizing vectorizers, namely Word2Vec vectorization and the elimination of English stop words. To produce a more precise forecast of false information, our proposed model also makes use of a variety of classifiers, including Stacked Bidirectional-Long Short-Term Memory (Stacked Bi-LSTM), Support Vector Machine (SVM), Decision Tree, etc. By padding each sequence to its maximum length, we offer the network normalized qualities that can affect how effectively and accurately it operates. The network then uses a Stacked BiLSTM to interpret this data. Using the fake news data set, we made predictions regarding the validity of news. Our testing has determined that the proposed solution is more accurate than other machine-learning models.
Ripeness Classification of a Bunch of Bananas Using a CNN

Bananas are one of the richest sources of vitamins and minerals in the world and bananas are also one of Thailand's most economically significant fruit crops. Images of a bunch of bananas were used to train a Convolution Neural Network (CNN). The convolutional layers applied different filters as feature detectors to create a feature map representing the detected input features. Then the features were shaped into one-dimensional features and sent to an Artificial Neural Network (ANN) for the banana ripeness classification. The results showed that the classification was quite accurate. Using the same set of images for both training and testing, the CNN achieved the accuracy of 100.00%. The average classification accuracy of 91.21% was achieved when testing with images excluded from training. The classification accuracy from class 0 to class 4 was 93.94%, 90.00%, 87.50%, 89.47% and 92.31%, respectively.
Heterogeneous Multi-Functional Look-Up-Table-based Processing-in-Memory Architecture for Deep Learning Acceleration

Emerging applications including deep neural networks (DNNs) and convolutional neural networks (CNNs) employ massive amounts of data to perform computations and data analysis. Such applications often lead to resource constraints and impose large overheads in data movement between memory and compute units. Several architectures such as Processing-in-Memory (PIM) are introduced to alleviate the bandwidth bottlenecks and inefficiency of traditional computing architectures. However, the existing PIM architectures represent a trade-off between power, performance, area, energy efficiency, and programmability. To better achieve the energy-efficiency and flexibility criteria simultaneously in hardware accelerators, we introduce a multi-functional look-up-table (LUT)-based reconfigurable PIM architecture in this work. The proposed architecture is a many-core architecture, each core comprises processing elements (PEs), a stand-alone processor with programmable functional units built using high-speed reconfigurable LUTs. The proposed LUTs can perform various operations, including convolutional, pooling, and activation that are required for CNN acceleration. Additionally, the proposed LUTs are capable of providing multiple outputs relating to different functionalities simultaneously without the need to design different LUTs for different functionalities. This leads to optimized area and power overheads. Furthermore, we also design special-function LUTs, which can provide simultaneous outputs for multiplication and accumulation as well as special activation functions such as hyperbolics and sigmoids. We have evaluated various CNNs such as LeNet, AlexNet, and ResNet18,34,50. Our experimental results have demonstrated that when AlexNet is implemented on the proposed architecture shows a maximum of 200× higher energy efficiency and 1.5× higher throughput than a DRAM-based LUT-based PIM architecture.
Comparison of Feature Extraction to Test Dryness and Moisture Levels in Burned Restoration Areas Using Linear Discriminant Analysis

The growth process after forest fires takes a long time because much land is often burned again, and some soils are less fertile. This research was conducted in Katingan, Central Kalimantan, as an area of interest. In this case, many human resources do not realize that they are trying to re-utilize the land that was burned by fires in that location. Land that has been burned should be rechecked a few months after the fire to see vegetation density. Using Landsat 8 OLI imagery to detect changes after the fire occurred, this research needs to compare feature extraction to test the level of dryness and humidity in the burnt restoration area. The feature extraction used is Normalized Difference Drought Index for drought detection and the Normalized Difference Moisture Index for detecting post-fire humidity. Furthermore, it will be tested using the Linear Discriminant Analysis algorithm to assess the classification of results from 1 June 2020 - 31 December 2020 in the area of interest carried out. The results of this research obtained for feature extraction NDDI has a lightness level with a range of 0.01 - 0.15 to 0.15 - 0.25, which indicates moderate to a severe drought that occurred in Katingan, Central Kalimantan, with a precision value of 98.33%, Recall 98.33% and Accuracy 98%. While NDMI has humidity in the range of 0.2 to - 0.4 to 0.4 to 0.6, which shows no growth to low growth with a precision value of 68.57%, Recall 68.57% and Accuracy 68% obtained.
Intrusion Detection System using Ensemble Learning Analytics

An Intrusion Detection System (IDS) monitors and analyses data to find any intrusions into a system or network. The network generates data at a tremendous volume, variety, and speed, making it difficult to detect attacks using conventional techniques like a virus detection system, misuse detection software i.e. the database of attack signatures that it uses to compare packets. Despite the researchers' significant efforts, IDS still struggles to identify new intrusions, to improve detection accuracy, and to reduce false alarm rates. To overcome the problems mentioned above this paper proposes an unique model named Intrusion Detection System using Machine Learning Analytics (IDSMLA), which uses SMOTE oversampling technique to deal with class imbalance problem, it also uses Minimum Redundancy Maximum Relevance (mRMR) to perform feature selection as feature selection reduces time complexity by eliminating irrelevant features and hence increasing the accuracy of the model and finally to perform classification task, the proposed model IDSMLA uses Extra Trees(ET) bagging ensemble technique. The performance of the proposed model IDSMLA is measured using accuracy and F1-score using 10-folds cross validation. Experimental results have demonstrated that the proposed model IDSMLA greatly outperforms different single-classifier based models, different ensemble models as well as different models present in literature.
Effects of Particulate Matter Pollution in Predicting Solar Radiation: A Case Study of Eastern Cape, South Africa

Increasing air pollution reduces surface solar radiation, making it one of the most important factors when estimating monthly global solar radiation (MGSR). The artificial intelligence techniques (artificial neural networks and support vector regression) were compared in this study to ascertain which of the particulate matter has greater effect on the prediction of solar radiation in the Eastern Cape, South Africa. The artificial intelligence models were trained using monthly data from January 1990 to September 2011 and tested using data from October 2011 to December 2020. The data was analyzed using statistical methods such as mean absolute percentage error, coefficient of correlation, and coefficient of determination. It can be concluded that incorporating the sea salt mass concentration of $\mathbf{PM}_{2.5}$ yielded a better precision when estimating the MGSR in Eastern Cape of South Africa.
Analysis of Traffic Sign Recognition for Automated Transportation Systems Using Neural Networks

Recently, numerous applications of Intelligent Transportation Systems (ITS) have gained increasingly more focus. One of the most crucial functions of ITS is Traffic Signs Detection and Recognition (TSDR) by notifying drivers of the status of road signs and providing helpful information regarding safety procedures which helps to improve safety. This paper analyzes the different deep learning algorithms implemented for TSDR along with discrete optimization functions used, Image conditions of the datasets, and accuracy attained during the training and testing of Traffic Sign Detection algorithms (TSD) in recent research. A comparison of all the approaches is presented in the study with the help of tables. In the paper, a list of publicly accessible datasets is also mentioned, along with a discussion that can be very useful for future TSDR research work.
An Efficient Network Intrusion Detection System for Distributed Networks using Machine Learning Technique

The task to ensure security in a network that is distributed over several nodes is a significant and challenging one. Since the primary objective of a DDoS attack is to prevent authorized nodes from gaining access to the service, this type of attack presents a significant threat to distributed networks. It is highly important that a modular and dependable NIDS must be created for handling DOS attacks in the distributed environment effectively, and in turn, all the nodes are available in the distributed network.The high need for modular techniques required in the detection phase for collecting, storing and analyzing the big data from the nodes in the distributed network poses significant hurdles in finding out the Distributed DOS attack.This research proposes a Big Data-based Distributed Denial of Service Network Intrusion Detection System to address these issues. Important features of the proposed intrusion detection system include a module for detecting network traffic and another for collecting data on that traffic. In this study, micro-batch data processing is employed for traffic feature gathering in the Network collection module and Random Forest (RF) algorithm-based classification technique is used in the traffic detection module for feature selection. For Storing a large number of wary attacks, Hadoop File System (HDFS) is used, and for accelerating the speed of data processing, S park is used as a suggested solution.The method was assessed using the NSL-KDD benchmark dataset to find the accuracy and many other parameters. Experimental results for Accuracy, Recall, F1-Measure and Precision, from the proposed work are compared to those from the machine learning techniques, DT(Decision Tree), PCA RF(Principal Component Analysis Random Forest), NB(Naive Bayes), SVM(Support Vector Machine), and LR (Logistic Regression). According to the experimental findings, the suggested detection algorithm achieved an Accuracy of 99.89%, respectively.
A Travelling Salesman Problem Approach to Efficiently Navigate Crop Row Fields with a Car-Like Robot

In recent years, interest in the use of mobile robots in the agricultural industry has increased, both to address labor shortages in rural areas and to increase food production in a more sustainable way. In order to have an efficient navigation system to cover long crop row fields, a path planner algorithm must consider maneuvering restrictions of the targeted robot. Most state-of-the-art works in agricultural navigation systems are intended for robots with a high degree of maneuverability that can typically make in-place turnings. This work aims to fill the gap in terms of the development of an efficient navigation system for car-like robots with limited turning radius in crop row fields. For this, we combine the global path planner A* and the local trajectory planner Timed Elastic Band (TEB). Additionally, we state the problem of finding an optimal path that covers the entire field as a Travelling Salesman Problem (TSP) that is based on the different turning maneuvers the robot can perform at field headlands. The solution of the TSP results in a time efficient coverage strategy that aligns with the robot's kinematics. Experiments performed in the Gazebo simulation environment show a reduction in field completion times of up to 20%, compared to trivial coverage paths. On the other hand, deviation of the robot with respect to the center of the field furrows was in all cases less than 10cm, which proves that the entire system operates with sufficient accuracy to avoid damaging the crops.
Robust Assessment of Dysarthrophonic Voice with RASTA-PLP Features: A Nonlinear Spectral Measures

This paper presents an artificial intelligence based speech signal processing technique to identify dysarthrophonic voice with relative spectral-perceptual linear prediction (RASTA-PLP) features. Dysarthria is a neural motor speech disorder caused by muscular weakness. Voice analysis of dysarthrophonic patients is challenging as this disease has multidimensional effects on the human voice generation system. Conventional spectral analysis is unable to accurately characterize the pathology associated with nonlinear dynamicity of human voice. This work investigates the suitability of RASTA-PLP features excerpted from speech signals to identify dysarthrophonic patients. The speech samples of healthy and dysarthrophonic patients are collected from the Saarbrücken Voice Database (SVD). Several machine learning and Artificial neural network (ANN) based algorithms are developed to evaluate the classification performance of the proposed system. The designed system can achieve excellent performance in terms of accuracy (100%) considering female and male subjects separately.
Cryogenic In-memory Binary Multiplier Using Quantum Anomalous Hall Effect Memories

Cryogenic memory technologies are garnering attention due to their natural synergy with quantum-computing systems, space applications, and ultra-fast superconducting processors. A recently proposed device, based on a twisted bilayer graphene (tBLG) on hexagonal boron nitride(hBN) shows immense promise as a scalable cryogenic memory. This device exhibits two topologically-protected variation tolerant non-volatile resistive states governed by the quantum anomalous Hall effect (QAHE). The implied memory states are read by the direction of the Hall voltage appearing across the two terminals of the device. The four terminal structure of the device and the Hall voltage property can be utilized to design a compact memory array suitable for in-memory computing. In this work, we design a simple in-memory binary multiplier, otherwise a complex circuit with traditional technologies, by utilizing the series addition of Hall voltages in the memory array. In addition, our novel in-memory binary-multiplier does not explicitly change the memory array architecture unlike DRAM in-memory multipliers. We also demonstrate bit-wise AND operation and partial product summation using our proposed design. Compared to a cutting-edge in-memory DRAM implementation our design is highly compact and significantly reduces processing complexity. Our simulations show an ultra-low power budget of 52nW /bit multiplication. Our designs demonstrate that QAHE devices are powerful candidates for future cryogenic in-memory computing.
Improved LVRT Performance of Direct Drive Wind Turbines by Torque Limitation and DriveTrain Damping Control

Intermittent nature of wind energy conversion system (WECS) can influence the stable operation of grid and the transient events in grid system can influence the electrical and mechanical components of the wind turbines. Low voltage ride-through (LVRT) is among the important grid connection requirement for wind energy conversion systems for a stable grid. Traditionally, the wind turbine satisfies the LVRT criterion by using the braking chopper which stresses the electrical system. In this paper, a torque limitation and drivetrain damping control are proposed as an add-on feature to the traditional braking chopper control which ensures better LVRT capability without overloading the braking chopper and the mechanical system. The proposed control method is verified by using a 2 MW model of a direct wind turbine developed in MATLAB Simulink and performance is compared with conventional braking chopper and torque limitation methods.
Microwave Heating Chamber Loaded with Photonic Crystal

Traditional microwave heating reaction chamber has low heating efficiency and poor heating uniformity. These deficiencies will lead to low productivity and decreased quality of the products. Therefore, the heating efficiency and heating uniformity are two important factors in evaluating the heating process. In this paper, the periodically arranged photonic crystal structure is introduced into the microwave heating chamber, making the electromagnetic wave scattered and reflected in the periodic photonic crystal, so as to achieve the effect of improving the heating efficiency and heating uniformity for the microwave heating system by changing the electromagnetic field distribution in the chamber. In the first part of the paper, the energy band structure of Al2O3 ceramics is obtained by solving the eigenfunction of photonic crystal, and by adjusting the size of photonic crystal structure so that the microwave heating frequency band (2.4GHz-2.5GHz) is in the photonic band-gap. In the second part of the paper, we construct the photonic crystal microwave heating chamber and describe the microwave heating control equations and the information about the parameters needed for the chamber. In the third part of the paper we simulated five heating modes, and by comparing the five modes with each other, we find that the heating effect of microwave heating is better when the heated object is rotating heated in the photonic crystal structure. And the comparison shows that the heating effect can be obviously improved after adding photonic crystals in the chamber.
A Single Phase DC-AC Converter using Dual Active Bridge fed Unfolder Circuit with Current Stress Minimization

Dual Active Bridge (DAB) fed Unfolder circuit can be used for direct integration of a DC source with single phase AC grid for battery charging or grid support applications. However, with conventional Single Phase Shift (SPS) control of DAB, high circulating current flows within the circuit leading to increased conduction losses and current stress in the switches. In this work, a new EPS (Extended Phase Shift) based control strategy is being proposed to minimize the current stress in the DAB. The proposed method has substantial advantage in reducing the peak current, which is caused due to the pulsating dc voltage of the shape of a rectified line frequency sine wave at the output of the DAB. In addition, a method to incorporate the current minimization algorithm within the closed loop control is also proposed. Along with this, the size of the dc link capacitance and the filtering requirement in the ac side also gets reduced because of the Unfolder circuit present at the output of the DAB. The proposed strategy has been explained and analyzed in detail and verified using MATLAB software.
Robust Dual-Current Control of a Three-Phase Grid-Tied Inverter under Unbalanced Grid Voltage Using LMI Approach

In this paper, a robust dual-current control is proposed for a three-phase grid-tied inverter under unbalanced grid voltage. To compensate the unbalanced grid current rooted by the grid voltage dips or faults, an additional negative sequence compensator is considered in this proposed controller. The current control for both positive and negative sequence utilizes state feedback with integral control to archive fast reference tracking without steady-state error. The robust control gain is computed by solving a linear matrix inequality-based optimization problem to obtain fast convergence rate in the presence of the uncertainty of the system. The positive sequence current reference is used to regulate the amount of current flow from DC source to the grid, and the zero negative sequence reference is used to compensate the unbalanced grid current. To verify the efficacy of this control method, a comparison between the proposed robust dual-current control and robust current control without negative sequence compensator is conducted using simulation.