Miracle-dz/UniFAR-FTUs huggingface dataset:
Early Detection of Thoracic Disease using Segmentation based Deep Convolutional Neural Network

The area of medical imaging has benefited greatly from deep learning. Thoracic issues can be fatal if they are not promptly identified and treated since they can be quite significant. Numerous thoracic illnesses can be diagnosed using chest X-ray imaging. We frequently needed a qualified and experienced radiologist to assess chest X-ray images and determine whether or not a person has certain thorax abnormalities. Because prompt diagnoses are sometimes necessary, not everyone has the chance to see an expert about treating chest disease in a timely manner. In order to help clinicians make important decisions, we outline a technique that uses picture classification and segmentation to detect a specific thoracic condition and produce a mask. Deep Learning techniques have outperformed several state-of-the-art models in a variety of scenarios, demonstrating their value. With ResNet serving as the foundation, we achieved successful outcomes using the U-Net architecture. U-Net performs better in the areas of semantic segmentation and medical image processing
Semantic Segmentation Network with Adaptive Neighborhood Construction for 3D Point Cloud

In the process of point cloud feature extraction, the inconsistent local spatial density of the input points will lead to the instability of Information fusion in the neighborhoods. In this paper, an adaptive neighborhood construction algorithm is proposed to solve the problem of uneven density when constructing spatial neighborhood from point cloud, which can more accurately capture the local spatial geometry near the query point. In addition, we augment the attentive pooling with differential attentive function, and both are integrated to assign a more effective attention score to the encoded feature. We applied the above methods to the RandLA-Net and tested on S3DIS Area5. The experiments show that our methods can effectively optimize the result of semantic segmentation and have obvious advantages in terms of accuracy and robustness.
Techno-economic Evaluation of Power System by Optimal Placement of FACTS Devices

The techno-economic evaluation of a power system is a complex process that involves evaluating various technical, economic, and environmental factors. This work considers an IEEE 14 bus test system for techno-economic analysis. The system is examined both with and without FACTS devices. The control parameters are chosen by using optimization methods. In this work, WOA is proposed and its performance is compared against two other methods such as PSO and GWO. To validate the efficacy of the WOA method various loading cases have been considered. According to the results of the simulation, the WOA method significantly lowers operating costs when compared to other approaches.
Bionic Robots as a New Alternative to Guided Dogs

As the robot industry grows, research into biomimetic robots continues to increase. Robot dogs are one of the more researched types. Unlike robotic arms and vehicles, robotic dogs emphasize interaction with people, and therefore their applications are more focused on daily life. Machine guide dogs are one application that makes good use of this feature. This paper describes the use of the robot dog DOGZILLA S1 for route patrol as well as obstacle recognition. Based on this, the robot dog will provide feedback to people, which can be used as pre-research for designing it into a complete guide dog.
DER-based Differential Testing of Certificate V alidation

The authentication of an X.509 digital certificate in the Secure Socket Layer or Transport Layer Security (SSL/TLS) protocol implementation is key to ensuring network security. Therefore, it is important to verify that the digital certificate can be validated correctly. Existing differential testing techniques based on certificate mutation are effective in detecting bugs in certificate validation, but mutation strategy is single. We address this problem by introducing our approach, a differential testing framework based on the mutation of Distinguished Encoding Rules (DER) encoded digital certificates. We obtain the DER-encoded X.509 digital certificate from the network as seed certificates, parse the label, length and value structure of seed certificates, and perform value mutation and Object Identifier (OID) guided mutation according to the parsed structure. We applied our approach on testing 3 popular SSL/TLS implementations, compared with the state-of-the-art differential testing technique (i.e., SADT). Experimental results show that our method is superior to SADT in detecting discrepancies.
Forecasting Stock Prices using Gated Recurrent Unit with the Help of Feature Engineering

Forecasting the stock prices is one of the hardest tasks because of its volatility and uncertainty, where additional features such as foreign exchange impacts the stock markets. Introducing AI in forecasting has helped in prediction tasks, especially the time series algorithms. This study attempts to implement various regression models such as the ARIMA model, which gives us improved results for data of linear nature but it suffers from conditions such as non-linearity, unable to cope up with dynamic changes in prices, and overdependence on historical data. This study attempts to implement ML and DL mechanisms to predict stock prices. As most stock markets deal with probabilistic functions and mathematical computations, usage of technologies such include algorithms for machine learning and deep learning. The proposed objective is to forecast the stock prices to enable the users to make decisions and undertake profitable trades within certain intervals by using GRU (Gated Recurrent Unit).
Research on remaining life prediction of bearings based on CDBN-BIGRU

Bearings are widely used in various fields of industry, and their reliability will directly affect the performance and lifetime of equipment as they are wearable components and need to be replaced regularly. So the study on remaining life prediction of bearings has great research value. We propose a CDBN-BIGRU bearing remaining life prediction model based on artificial intelligence methods, which achieves the feature extraction and information filtering of vibration signals, and establishes the time series relationship between the signal features, so as to reflect the operation of bearings in different time periods and the intrinsic correlation in a more detailed way. This method effectively improves the prediction accuracy belongs to the remaining life of bearings. The experiments show that CDBN-BIGRU model we proposed get better result than other methods, which improved the accuracy and F1 score value. Based on the current research progress, We will continue to promote the research on the robustness of the remaining life prediction model in the future work.
Robotic Inspection and Subsurface Defect Mapping Using Impact-Echo and Ground Penetrating Radar

Concrete infrastructure often develops a variety of internal flaws that cannot be detected through visual inspection alone, and must be regularly inspected with other methods to maintain structural integrity. It has been demonstrated through previous studies that relying solely on a single non-destructive evaluation (NDE) method can be insufficient in providing a comprehensive evaluation of the structure's condition. In addition, manual NDE data collection can be labor-intensive for on-site engineers. This paper presents a robotic inspection system that uses vision-based positioning and tags NDE measurement with pose information to reveal and map subsurface defects. The system consists of three modules: 1) an Omni-directional robotic data collection platform equipped with a Realsense D435i camera for localization, an impact-echo (IE) sensor, and a ground penetrating radar (GPR), to perform automatic NDE data collection; 2) an IE data processing module that utilizes both learning-based and classical methods to interpret the IE data and reveal subsurface objects; 3) a GPR data processing module to reconstruct underground targets and create a 3D map for better visualization. Field testing demonstrates that the robotic system significantly increases the data collection speed, and the correlation of findings from both IE and GPR sensors give a comprehensive evaluation of concrete structures that will benefit the inspection and maintenance industry of civil infrastructure.
A Study on Various Electric Vehicle Charging Technologies

In these days, Electric Vehicles (EVs) are becoming more essential due to various factors, in the aspect of less price as well as natural environmental awareness. Also, they are reducing dependency on fossil fuels and greenhouse gas emissions. In this paper, we are going to study different types of charging methods with their advantages and limitations. The charging methods of EVs are classified into portable and non-portable charging. Depending on the various modes of charging, there are also slow charging, fast charging, wireless charging, etc. Moreover, we analyze the different standards that are available for the electric vehicle charging process.
Deep Learning Based Approach for Plant Leaf Disease Detection for Smart Farming

An emerging concept in farming, Smart farming, refers to farming technique using technologies like AI, cloud computing. IoT, drones and robotics. Smart farming totally changes the way agriculture is thought of, helping increase quality and quantity of food products. It also helps optimize the human labor required for production. With the ever increasing population and ever increasing demand for food production, it becomes very much essential to save even a morsel of food. In such a scenario, it is very much required to ensure that the crops are healthy and devoid of any diseases. Plant diseases can reduce the availability of food; it can even destroy the entire crop field affecting the yield. The age old scheme of plant disease detection through bare eye observation by experts requires a large team of experts and continuous monitoring of plants that incurs huge costs with large farms. In this paper, a model based on deep learning has been proposed that is able to classify a plant as healthy or diseased based on the prediction made on the leaf condition with a very high accuracy.
Training of the FMEA in a Virtual Learning Environment

The sustainable teaching of quality methods in the sense of Lean Management and Six Sigma with the help of assistance systems, such as virtual reality glasses, represents a new and growing aspect of continuing education programs. The development and use of virtual learning environments offers the chance to deepen the theoretical prior knowledge through interactive learning opportunities. In this way, existing learning concepts are supplemented with virtual teaching content. Complex or difficult to present learning settings can be mapped virtually without high material consumption or operating costs. In this paper, the reference to quality methods for teaching with virtual reality is established and a classification in the Assisted Reality Implementation Model is made. Subsequently, the requirements for the programming of a virtual learning environment are shown. Using the example of the quality method Failure Mode and Effects Analysis, the procedure and implementation will be explained and the research question will be examined whether a reasonable implementation in virtual reality is possible. Special attention is paid to the technical implementation and the requirements for the design of the virtual environment. In order to be able to answer the research question, the question of acceptance is put in the foreground and this is carried out with the help of a questionnaire. In the following, first results of the explorative tests from used questionnaires are presented to get an overview of the explorative acceptance.
Improved Model - Free Sliding Mode Control Of PMSM Based On Finite-Time Generalized Proportional Integral Observer

Aiming at the problems of poor control performance and poor anti-interference performance of PMSM control system due to external load disturbance, model and parameter uncertainty, a composite control method of PMSM based on improved model-free sliding mode control is proposed. Firstly, the ultra-local model of PMSM system without considering motor parameters is established. Then, a model-free single-loop sliding mode speed controller is designed according to the ultra-local model of the system. Furthermore, for the total disturbance of the system, a finite-time generalized proportional integral observer (FT-GPIO) is established, which can estimate the total disturbance of the system more accurately and stably, and perform feedforward compensation to update the control information in real time. Finally, compared with the traditional control method, the simulation results show that the proposed control method has good dynamic performance, anti-interference ability and steady-state control accuracy.
Method for Landing an Orbital Aircraft on a Low-Flying Vehicle with a Dynamic Principle of Support

A method and device are proposed for landing an aerospace aircraft by docking with a heavy ekranoplan, allowing them to land together on a water or other flat surface. A useful result is an increase in the payload of the orbital aircraft due to the absence of a wheeled chassis and the elimination of the need to build a special expensive runway for its landing.
Application of Machine Learning in Driver Drowsiness Detection

In today’s world, road accidents are mainly caused due to drunken driving, or a driver being fatigued. Therefore, the best way to judge whether the driver is feeling fatigue or not is by checking the state of the driver i.e., drowsiness. With the increase in the road accidents, driver drowsiness detection has become an important factor and is widely accepted. Determining the number of accidents caused by driver drowsiness has become quite difficult as it is not considered most of the time. The shift from feeling fatigue to snoozing usually goes unnoticed by the driver. This led to the requirement of addressing this issue by creating a driver drowsiness detection system to decrease the accidents caused due to drowsiness. Few parameters should be considered to develop such application. One of these parameters includes counting the number of eye blinks in a particular period. The proposed work will keep a record of the eye movements continuously. If the driver is proven to be drowsy then a warning alarm will be initiated. To implement the proposed application, OpenCV library and ML algorithms have been used. This work will benefit in saving several human lives by avoiding road accidents.
A Systolic Array with Activation Stationary Dataflow for Deep Fully-Connected Networks

This paper presents an activation stationary (AS) dataflow suitable for networks with pure fully-connected (FC) layers. It is shown that the proposed AS dataflow can help to reduce the required memory size in hardware design and optimize energy efficiency by reducing data movement. Based on the AS dataflow, an output stationary (OS) systolic array is proposed to compute FC networks. To evaluate the proposed design, we further implement an accelerator for the FC-based implicit representation for MRI (IREM) algorithm. A proofof-concept demonstration system is developed based on field programmable gate array (FPGA). To evaluate the proposed design, We also map the IREM accelerator to 40nm CMOS technology and compare it with CPU, GPU-based and ASIC implementations.
Dielectric Constant Measurement Using Time-Domain Shifted Metal-Backing Measurements

A free-space microwave method is proposed for accurate dielectric constant <inline-formula> <tex-math notation="LaTeX">$\varepsilon _{r}$ </tex-math></inline-formula> measurement of flat dielectric samples using time-domain measurements. It does not require any reference data (calibration-independent) and thickness information for <inline-formula> <tex-math notation="LaTeX">$\varepsilon _{r}$ </tex-math></inline-formula> measurement. Besides, it uses only two power peaks of reflected signals of one sample with offset metal termination. It is first validated by simulations and numerical analysis. Free-space <inline-formula> <tex-math notation="LaTeX">$\varepsilon _{r}$ </tex-math></inline-formula> measurements of three low-loss dielectric samples [polypropylene (PP), polyethylene (PE), and polyoxymethylene (POM)] are then performed for validation of our method. Its accuracy is compared with the accuracies of similar methods in the literature.
Comparative study of the MPPT methods applied to the PV system; Perturbation & Observation technique, sliding mode control and fuzzy logic control

The objective of this research paper is to investigate various methods for maximum power point tracking (MPPT) that can be applied to photovoltaic (PV) systems. The study involves the assessment and comparison of three different techniques: perturbation and observation (P&O), sliding mode control (SMC), and fuzzy logic-based MPPT. To transfer the maximum amount of power possible, a DC/DC boost converter is used between the PV system and the load. It has been observed that conventional MPPT approaches such as P&O are unable to detect and track the highest peak, leading to a significant loss of power, so a technique is suggested sliding mode control, which offers many benefits such as durability against parameter changes, minimal current output distortion, and excellent reference tracking. The problem with this technique is high oscillations in the transitional regime. The algorithm based on fuzzy logic control is reliable and effective. The simulation results shown in MATLAB confirm that the fuzzy logic method does operate at the ideal point without oscillations. Moreover, it exhibits good transient behavior and better tracking of the maximum power point.
M-Net: COVID-19 Lung Image Classification based on CNN and Transformer

The M-Net model is proposed in this paper to classify the COVID-19 lung CT images of patients. The mixing module is presented in this work to extract local information while capturing long-range dependencies. The introduction of the mixing module makes an improvement to the classification performance of the model. Experimental results show that the proposed M-Net model outperforms other state-of-the-art classification models, including both CNN-based and Transformer-based models, with an Accuracy of 95.03%, a Precision of 95.21%, and a Sensitivity of 97.46%.
MPPT control of photovoltaic power generation system based on LESO estimation error compensation

In order to ensure that photovoltaic power generation systems can quickly reach the maximum power point and operate smoothly, this paper designs a maximum power point tracking (MPPT) controller based on estimation error compensation of linear extended state observer (LESO). The purpose of improving the structure of first order linear active disturbance rejection control (LADRC) is to eliminate the estimation error of LESO in LADRC. By adding an estimation error compensation term to the traditional control law, the overshoot of the system is reduced, the response speed is increased, the anti-interference performance is enhanced, and the utilization rate of optical energy is improved.
Estimated Parameters Of Auto Regressive eXogenous Model Using Two Stage Least Square and Recursive Least Square Algorithm

In this investigation, output error models based on two-stage and auxiliary models are investigated with those based on least squares and recursive least squares algorithms. The main principle is to use the decomposition technique and the auxiliary model identification notion to separate a system into two subsystems, each of which comprises a parameters variable. Then, employ each algorithm separately. Root mean square error is applied to calculate model validation procedures. Compared with the two stage model-based recursive least squares algorithm, the TS-RLS algorithm has more parameter estimated accuracy.
Mine & Rock Prediction by Ensemble Machine Learning Algorithms

Scientists and the military use submarines to go deep under the ocean water. Defense use submarines to guard the oceans. It is usually enormous and provide greater security. But marines have threats as obstacles and may be mistakenly recognized the rock as mine or mine as a rock. The SONAR technique has exploited the discovery of rocks and minerals. Objects can be detected using high-resolution images, but unique methods are required for identification. This study works on a dataset that differentiates the rocks and mines using SONAR signals. Under 60 various angles using SONAR signals, frequencies are recorded. Machine learning standalone classifiers, cross-validations, and bagging classifiers are applied on the data set to build the models. Standalone algorithms have challenges like overfitting, under fitting, feature selection, generalization on new data, etc. Bagging with cross-validation on standalone algorithms overcome overfitting, stability, bias reduction. The objective is to improve the model performance, enhance reliability and generalization ability of machine learning models. Random forest works best in all categories compared to other algorithms.
4b/4b/8b Precision Charge-Domain 8T-SRAM Based CiM for CNN Processing

Compute-in-memory (CiM) is a promising solution for solving the bottleneck of frequent data movement between the memory and processor in Von-Neumann architecture. In conventional multi-bit CiM architecture, when computing N-bit input and N-bit weight MAC operation, 2N 1 cycles are needed for N-bit input modulation and normally 3-4−cycles with complex switch operation are needed for N-bit weight realization, which significantly degrades the final throughput and power efficiency. In this work, a C-2C DAC built in the 8T SRAM CiM array is designed for 4-bit weight and 4-bit input MAC operation, which can be completed in just one cycle. In the final power efficiency evaluation, our 4b/4b/8b CiM architecture attained up to 640 TOPS/W (normalized to 1b/1b/1b precision) which is a 6-10 times improvement as compared to the conventional multi-bit CiM architectures. The proposed architecture with 4b/4b/8b precision can provide 91.47% and 68.10% accuracy on CIFAR-10 and CIFAR-100 dataset classification, respectively.
Development of a Bengali Speech-based Emotion Analysis System

Detecting emotion from speech is one of the most demanding works in speech recognition and processing. This paper presents speech emotion recognition in the Bengali language by extracting features from speech samples and matching them with a feature database. In this work, five (5) classes of emotions are detected using Deep-Learning and Machine-Learning based algorithms on a data set in Bengali. The classes of emotion defined are Happiness, Sadness, Threat, Panic, and Neutral. The extracted features are Mel Frequency Cepstral Coefficient (MFCC), Chromagram, Mel- spectrogram, spectral roll-off, spectral centroid, and Teager Energy Operator (TEO) is applied to them to enhance the details of extracted spectral features, increasing the algorithm’s accuracy. Simulation results show that the devised method outperforms the existing speech emotion recognition method.