Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-frame Image Super-resolution Reconstruction Based on SIFT

Accurate sub-pixel image registration is a key problem in image super-resolution reconstruction. Optical flow methods based on pixel feature, which are widely used in image super-resolution reconstruction, are difficult to achieve registration of sub-pixel accuracy for large motion field. This paper considered a robust multi-frame image super-resolution reconstruction method based on SIFT. Firstly, SIFT operator was used to pick up keypoints and their descriptors of input low-resolution image pairs which are to be registered. Then the candidate keypoint pair was selected, outliers were wiped off through RANSAC, and images pair displacement was computed at the basis of assumed transitional geometry constraint model. Secondly, initial reference frame was selected from vision center frame or specified image frame. Lastly, super-resolution reconstruction was done through conventional super-resolution reconstruction framework. Experimental results show that the proposed image super-resolution reconstruction method based on SIFT is feasible, and the quality of super-resolution reconstructed images is better than those of classical methods by both subjective evaluation and objective standards.
Design of an Automation System for the Production of Soil-Cement Blocks

The emission of greenhouse gases produced by industries is an increasingly worrying factor in the world, as these gases destroy the ozone layer, including the emission of CO2 from the manufacture of handmade bricks with kilns during the vitrification process. The objective of this research was to determine the compressive strength and its analysis of the cost of manual manufacture of an ecological, sustainable material such as soil-cement blocks; in addition, an automated system was proposed for the production of these blocks using engineering software such as Factory IO and TIA Portal. As a result, it was obtained that with a 15% cement content in the blocks, an average mechanical resistance of 8.47 MPa was achieved; however, the manufacturing cost was minimally higher than that of industrialised bricks by 4.17%. Finally, it was concluded that blocks with 15% cement are an alternative for housing construction due to the high compressive strength, the automation to manufacture these blocks makes the cost lower due to the reduction of skilled labour. Likewise, it has been demonstrated that a mechatronic control by means of a PLC provides a productive process of soil-cement blocks because it improves and accelerates the manufacturing processes of this complex product, all by means of the use of multiple sensors and tank filling times depending on the process.
Predicting Stock Price Movement for The Inefficient Market: Case of Hanoi Stock Exchange (HNX)

If the market is efficient, with stock prices accurately reflecting the true risk of an investment, then the issue becomes simpler. While this is true, investors may have a window of opportunity to discover a successful investing strategy if the market is inefficient. The primary goal of this research is to use the Support Vector Machine (SVM) algorithm to predict daily cycles of price increases for the ten largest-cap companies trading on the Hanoi Stock Exchange (HNX) over the Covid-19 timeframe (January 1st, 2019, to December 1st, 2022). Study how the model performs when trained and tested with a moving window. The outcome was an impressive average accuracy of 81.68 percent for the predicting model.
Implementation of Liver Segmentation from Computed Tomography (CT) Images Using Deep Learning

Liver segmentation from computed tomography (CT) images has grown significantly in importance in the field of medical image processing in the last few years. It is the first and most crucial step in any computerized technique for the automatic detection of liver disease, liver volume measurement, and 3D liver volume rendering. The diagnosis and treatment of liver cancer depend heavily on the segmentation of the liver from CT images to get liver volumetric data, but manual segmentation is a strenuous and time-consuming process. The procedure can be accelerated, simplified, and made less error-prone by using deep learning methods. Image segmentation based on deep learning techniques has gained widespread acceptance due to its robustness, efficiency, and it's reproducible nature. Therefore, in this paper, using UNet, MONAI (Medical Open Network for Artificial Intelligence) and PyTorch framework, a deep-learning model to segment the liver from publicly available CT scan dataset was developed. The same ideas that underlie this model for segmenting the liver will allow to create models for segmenting other organs or malignancies using CT data. The goal is to develop a liver segmentation model that can quickly and accurately extract the liver from any given CT image with an accuracy that is on par of manual segmentation performed by a skilled radiologist.
Design and Implementation of an Automated Control System for Anomaly Detection Using an Enhanced Intrusion Detection System

In forest zone and the field of agriculture, the conflict between human beings and wild animals is a vital problem to be addressed since it results in the extinction of various resources and the lives of human being is also in danger. The farmers are the major victims in such issues where they lose their crops to be cultivated, livestock, other related properties and resources. Henceforth, such regions and zones should be monitored round the clock to avoid the intrusion of any wild animal that could probably create a negative impact in the field and human lives. The Smart intrusion detection system can be implemented in the field with sensors and controllers that can detect the presence and movements of anonymous objects. This smart system will be implemented with a mechanism to detect and classify the objects with deep learning algorithm on further running of the system, using Content-Based Image Retrieval (CBIR) the captured image is investigated and classified based on the extracted features and alarm only if the intruder is recognized to be an unauthenticated object. Instant notifications will be triggered to the farmers through their mobile applications and the intruder will be reinforced to move out of the field with the alarming sound, which could annoy the object based on its type.
A Gradient Boosting Classifier to Predict Electric Power Consumption by Nuclear Power Plant

A nuclear power plant (NPP) purchases electricity from the integrated energy system to meet all of its demands during operation. On the basis of anticipated values, electric power is purchased. To adequately meet plant demands, it is crucial to predict the consumption of electricity as precisely as possible. The more exact the forecast, the better the financial outcome. The dataset utilized in this research is a time series of power consumption values in Megawatts (MW) which ranges from 203.2208 MW to 216.4605 MW and 744 samples were collected in total from the observations of electric power consumption (hourly) by NPPs. The entire data needs to be preprocessed and some Gradient Boosting approaches including Stochastic Gradient Boosting (SGBoosting), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM) were applied while maintaining the 70%-30% training-testing ratio of the dataset. XGBoost classifier demonstrates 99.81% ac-curacy for predicting power consumption (with 0.982 Coefficient of Determination, 0.0044643 Mean absolute percentage error, and 0.0668 root mean square error), which is better than other models (SGBoost and LightGBM). This approach will be evaluated later with more electric power consumption data by nuclear power plant.
Multi-step Road Network Speed Prediction Based on Graph Convolution Long Short-Term Memory Neural Network

Accurate and efficient short-term traffic flow prediction is one of the basic guarantees for solving various traffic problems. According to the road network topology and the temporal and spatial correlation of traffic flow, a multi-step road network speed prediction method based on graph convolution-long short-term memory neural network (GCLSTM) is proposed. Firstly, the weight parameters of the convolutional layer are designed to increase the connection between the convolutional layers. Convolutional layer is embedded in the gating unit of long short-term memory neural network (LSTM) to achieve the effect of capturing the spatial and temporal correlation at the same time. Secondly, an encoder and decoder structure is used to realize multi-step prediction. The structure gradually increase the output sequence length to improve the efficiency of model training. Finally, ablation experiments and comparison experiments are performed on the expressway dataset and the urban road dataset. The ablation experiments show that the error index of the hybrid network prediction model is significantly reduced. The comparative experiments exhibit that the GCLSTM prediction model has higher multi-step prediction accuracy and better prediction performance at a single observation point. This model provides accurate prediction information for intelligent transportation.
Comparison of Machine Learning Algorithm for Stock Price Prediction Using Sentiment Analysis

Forecasting companies' stock market prices are one the interesting topics for analysts and researchers. Although a company's stock price can be unpredictable, long-term forecasts can help but it is dependent on many factors such as the company's business model, change in leadership, and investors' mood. It has been found to be insufficient to predict stock values just on the basis of historical data or textual information. Previous research in sentiment analysis have shown a strong correlation between the movement of stock prices and the publication of news stories. At different levels, a number of sentiment analysis research have been attempted utilizing methods. In this paper, we made a comparison of various Machine Learning methods on five datasets of financial news related to the company and domains in which the company. Encouraging results are obtained using 13 models i.e., Linear Regression, Ridge Regression, Lasso Regression, Random Forest, Naive Bayes, Logistic Regression, LSTM, ARIMA, Logistic Regression, Support Vector Machines, Decision Tree, Boosted Tree, and ensemble method which depict polarity of news articles being positive or negative and the accuracies are 93.90%, 92.31 %, 92.27%, 85.44%, 84.65%, 84.65%, 94.73%, 90.13%, 82%, 83%, 72%, 70%, 95.11 % respectively.
Improved BEB Algorithm for Computer Network Optimization

This paper proposes an end-to-end delay optimization algorithm for distributed networks based on BEB algorithm. Aiming at the requirements of distributed two-level cooperative control on the communication quality and sampling period of wireless sensor networks, the end-to-end delay optimization method to maximize the packet arrival rate is adopted to obtain the minimum sampling period of the control system under the premise of ideal wireless communication soft QoS guarantee. The simulation results show the influence of network parameters such as packet arrival rate, maximum backoff window size, multi-hop number and packet size on the end-to-end delay performance of the network, and obtain the optimal packet arrival rate under the premise of satisfying the ideal wireless communication soft QoS guarantee.
Research on Real-Time Ship Detection Using Deep Learning

With the development of globalization, ship detection is of great significance for water traffic. The monitoring data of the camera are used to detect the ship. This paper introduces and compares three target detection algorithms: Yolov3, Centernet and Yolov5, and adds SE (Squeeze Excitation) module to Yolov5. Firstly, we preprocess the data set, and then carry out experiments to compare the above algorithms. Finally, four evaluation parameters: Precision, Recall, mAP_0. 5 (mean average precision) and mAP_05:0.95 are used to evaluate the performance of the model. At the same time, the processing time and parameters of the algorithm are compared. The results show that Yolov3 has the highest accuracy, but the parameter size is larger and SE-Yolov5 can not only consider the speed, but also maintain the above four performance indicators.
Quantifying Transformer and Cable Degradation in Highly Renewable Electric Distribution Circuits

Building electrification, vehicle electrification, and renewable distributed energy resources (DER) are all viewed as key technologies for reducing greenhouse gas and pollutant emissions. However, the added electrification may stress, damage infrastructure, and result in early replacement of electrical distribution system components. Conversely, DER may alleviate infrastructure strain, resulting in lower overall costs through delayed infrastructure repairs and upgrades. Regardless, the effect of electrification and high use of renewable DER are generally addressed qualitatively, not quantitatively. This paper presents a method to quantify the effects of electrification, DER, and other emerging clean energy technologies on local electric distribution infrastructure. This is accomplished by predicting the degradation of distribution transformers and power cables, followed by the optimal resizing of electric components such that cost is minimized. The method is demonstrated for two scenarios where the buildings and vehicles across a small community are electrified, resulting in accelerated distribution infrastructure degradation and replacement.
An Organized Method for Heart Failure Classification

Heart failure is a major health hazard, and millions all over the world are affected by this chronic syndrome each year. Predicting heart failure in patients is extremely tough and inaccurate. To improve the prediction success rates, the authors developed a machine-learning model with improved accuracy. In this paper, the authors have used the publicly available heart failure clinical records dataset, available on UCI repository, which contains medical particulars of 299 patients with heart failure. The authors have applied four machine learning classifiers: Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), and XGBoost to predict the survival chances of patients. SVM achieved the highest accuracy of 96.67% for patient survival prediction in comparison with all the other employed machine learning classification techniques.
A Survey of Saga Frameworks for Distributed Transactions in Event-driven Microservices

Event-driven microservices use events for inter-service communication. Such an asynchronous communication model preempts distributed monoliths and allows microservices to scale well and evolve autonomously. Typically, a message broker is employed to carry the events across the participating microservices. However, it is impractical for the event-driven microservices to accomplish the distributed transactions using traditional 2-phase commits. Hence, the microservice architecture advises implementing a distributed transaction as a saga of local transactions. Though a saga does not offer the automaticity and consistency of a traditional transaction, it indeed offers eventual consistency. The saga takes the form of either orchestration or choreography depending on the number of microservices that intend to collaborate. Sagas are used across various business domains such as e-commerce, banking, social media, logistics, etc. However, saga-based transactions require careful design of the individual microservices to take care of their interactions and error handling. It is even more tedious to implement Sagas in event-driven microservices because of the asynchronous nature of the interactions. Thus, it is always suggested to use a well-tested framework for implementing the sagas instead of building them from scratch. This paper surveys some of the available frameworks for saga implementation across three popular platforms namely Java, Python, and NodeJS. This survey finds the need for a vendor-agnostic abstraction for separating the transaction layer from the business layer.
Modern Face Recognition Attendance System Using Open CV

In this paper according to recent data, schoolchildren are the group most susceptible to social crimes that take place anywhere in the globe, including in our own country. On their way to and from school, many of these instances take place. Other times, social offence like sexual harassment happened to them when they were riding the school bus. We suggest a real-time monitoring system based on image processing methods because of how serious this problem. The media has made using a camera to identify a pupil more commonplace. This system monitors the bus cameras and examines the children’s movements. The technology recognizes student faces and records student ID numbers. The technology will also sound an alarm to notify the public if the situation is urgent. Technologies that can be used with Python are included in the Open CV package. Face detection was performed using the Haar-Cascades classifier, and face identification was performed utilizing Eigen faces. Using flowcharts, the system’s levels are represented. Furthermore, The automation attendance systems use face recognition. This eliminates the vast majority of problems that manual attendance systems.
Depression Prediction from Combined Reddit and Twitter Data using Machine Learning

Technology has transformed communication on a global scale, and social media platforms like Twitter and Reddit have become crucial forums for discussing current events and sharing views. These platforms offer ample data for extracting user opinions, which can be examined through natural language processing and text analysis methods. This project uses these techniques to systematically discover, gather, measure, and examine emotional states and personal information in posts on Twitter and Reddit. Our primary goal is to predict depression in these posts, which is a significant public health concern. To achieve this, we use a combined dataset of Twitter and Reddit data. Our research aims to add to the increasing field of investigation on using social media data to comprehend and forecast mental health outcomes. By identifying patterns and trends in language use that may be indicative of depression, we hope to provide insights that can inform the development of interventions and support for individuals at risk of mental health challenges.
Coprime Polynomial Based Dual-Circulant Modulation for Inherent Submodule Voltage Balancing in MMDC

The modular multilevel dc–dc converters (MMDCs) have attracted much interest in medium voltage dc applications, but have to face the main issue of balancing submodule (SM) capacitor voltages. This article proposes a dual-circulant modulation and proves it possessing the inherent balancing capability for arbitrary operation cases. High-speed communication for real-time sensor data transfer could be avoided, which reduces implementation costs and also enhances the system reliability. Two sets of circular switching patterns are preset and combined to complete the full constrains on SM voltages. The associated polynomial of circulant matrix is introduced and the coprime of polynomials demonstrates the full-rank feature of the extended switching matrix that promises the inherent balancing for any operation cases. Then, the switching patterns are reallocated and optimized to keep the SM uniformity and reduce the capacitor voltage ripple at the same time. Full-scale simulations on MMDC models and down-scaled experiments on prototypes are both presented, which validates the inherent voltage balancing capability and the optimization of SM capacitor voltage ripple with the proposed dual-circulant modulation.
Adaptive-Extremum Seeking Method for Impedance Identification in Distribution Grids

This paper proposes an adaptive Extremum Seeking Control (ESC) based method for extracting the impedance of a distribution grid. An adaptive version of ESC is developed for speeding up the impedance identification process by dynamically increasing the convergence rate of ESC. An impedance change can occur at any time and may produce a disturbance in the grid voltage. Disturbance caused by a change in impedance value can cause the Extremum Seeking Controller to re-adjust its operating point. At first, this may cause the controller to take huge steps in the direction of the new impedance value. Once the value comes closer to the new impedance value, the controller then steadily converges. This steady convergence (especially observed in the distribution grid) is often too slow and may cause an unnecessary delay in estimating the correct impedance value. This work proposes a method of observing the external disturbance and dynamically adjusting the gain of demodulating signal to make the convergence process faster while keeping the ESC-based impedance identification process stable. The increased convergence rate is achieved while keeping the magnitude of applied perturbation constant throughout the process and only changing the magnitude of the demodulating signal.
Boundary Strength based Adaptive Interpolation Filter

This paper presents an adaptive interpolation filtering scheme for the High Efficiency Video Coding (HEVC) standard. In regards to interpolation for motion estimation and compensation, the conventional HEVC employs 8-tap and 4-tap filters for luma and chroma samples, respectively. Coefficients in such filters are determined by discrete cosine transform (DCT). In the proposed scheme, boundary strength values are stored after the execution of the deblocking filter. For each block, the sum of boundary strength values is calculated to indicate whether its region is complex or simple. Consequently, based on the region classification, 12-tap and 8-tap interpolation filters are used for complex and simple regions, respectively. This process is applied to luma sample interpolation only. Simulation results show 1.8% average BD-rate reduction compared to the conventional method.
Multiband PIFA for Mobile Applications

A multiband PIFA antenna is described for mobile handset usage. The suggested PIFA antenna is intended for use in the frequency band from 1.8GHz to 2.4GHz. The proposed PIFA is useful for GSM1800 (1710-1880MHz), GSM1900 (1850–1990 MHz), UMTS (1920-2170MHz), WCDMA (1.9-2.18 GHz) and LTE (2.53GHz). The multiband PIFA antenna is designed by incorporating slots and loops in PIFA.