Miracle-dz/UniFAR-FTUs huggingface dataset:
Three-Lead ECG and Heartrate Variability Using AD8232

Over the past few decades, heart disorders have grown rapidly, and numerous people have died as a result of various health issues. Heart problems occurs due to the various reasons. With the early detection of the heart condition we can save many lives. Generally the heart functioning is measured by Electrocardiogram(ECG). ECG contains 12 leads which measure the overall condition of the heart considering the health factors. ECG analysis will help the person to take a preventive measure against heart attack. Taking an ECG frequently is impossible for the person, Because of the cost and the person cannot visit the hospital daily for the health check up. As heart is the major functioning of the human body, we cannot take it for granted. We came up with a solution to overcome the problem by sitting at home and monitoring the heart rate. As normal ECG consists of 12 leads, here we consider the three main leads(red, green and yellow) to monitor the health. Our proposed system consists of 3 leads as they are major in an ECG analysis. The main aim of these three leads is to record the electrical signals of the heart. The recorded ECG signals can be analyzed by using a serial plotter graph. The graph will give the Heart Rate Variability(HRV), which is an analysis of milliseconds variations in intervals between the heart beats. The electrical signals of the heart are measured and amplified by AD8232 sensor. Later, we interface the AD8232 sensor with the Arduino UNO and analyse the serial plotter graph. Then the information is passed to a mobile app using ESP8266(WiFi module) and comparing the information with the given ranges will predict the heart condition.
BCIRecog: An Optimized BCI System for Imagined Speech Recognition

Brain-computer interface (BCI) systems have garnered increasing interest from researchers in the past decade, as they hold the potential to improve quality of life and enhance productivity and performance across various domains. These systems primarily aid paralyzed patients who face communication difficulties by decoding their thoughts using brain signals. Despite a plethora of research works being proposed for imagined speech recognition using BCI, the crucial factor of prediction time is often overlooked. The BCI-based speech recognition models are expected to recognize the imagined thoughts in lesser time. Hence, in this paper, the primary focus is to increase the speed of the BCI-based speech recognition system by optimizing the training and testing time of the recognition models. Electroencephalogram(EEG) signals extracted from 23 participants while imagining digits, characters, and images using the imagined speech technique were utilized. A set of features extracted from the EEG signals were fed as input to the random forest classifier model. The classifier framework is optimized using the Intel® oneAPI tool for minimizing the training and testing time. The experimental results reveal that the optimized model performs 12 times faster than the unoptimized model. The proposed model can be utilized in future BCI research to attain optimal performance in time-sensitive applications.
Intrusion Detection for Internet of Vehicles using Machine Learning

The Internet of Vehicles (IoV) has replaced vehicular networks as the preferred paradigm as a result of the enormous expansion in computer and network capabilities. Because of the dynamic IoV’s diverse nature necessitates effective resource management, which calls for cutting-edge technologies like Software Defined Networking (SDN), Machine Learning (ML), and others. In Software Defined-IoV (SD-IoV) networks, Road Side Units (RSUs) are in charge of network effectiveness and provide a number of safety features. However, it is not practical to deploy enough RSUs, and the current RSU placement does not provide complete coverage of an area. Furthermore, any lapse in network security or performance has a negative influence on driving. Thus, the objective of this study is to increase security in an IoV network by using different types of Machine learning Algorithm to increase network efficiency. As a result, it is suggested to use XG-BOOST Learning Algorithm placement method to decrease communication time while expanding coverage among IoV devices. Along with this method, this paper is works on the IoV network by using CAN-OITDS Dataset. The comparative study of conventional ML algorithms shows that the IDS detects the malicious attack on IoV with the help of XGBOOST with high accuracy of 96.04%.
Intelligent Fertilizer Recommendations to Improve Crop Yield Using Wireless Sensor Network

For the expanding populace, farming is crucial in supplying food and other resources. However, the pressure on farmers to maximize crop yields is rising along with the demand for food production. Giving the crops the appropriate amount of nutrients at the appropriate time can help to achieve the goal. Artificial intelligence (AI) and wireless sensor networks (WSN) are two powerful technologies which can help farmers to achieve this goal to increase crop yield. In this article, a stacking method utilizing machine learning (ML) and artificial intelligence (AI) is suggested to create fertilizer recommendations for agricultural operations. The aim of this study is to develop an intelligent system that utilizes data from senso and algorithms based on machine learning to optimize fertilizer application and boost crop yields. The study includes the use of the Ensemble learning technique to develop a fertilizer recommendation. It takes the prediction of base models SVM, Decision tree, and KNN as new features, then train the meta-model which is Random Forest classifier and makes predictions using the train meta-model with optimal performance.
A Deep Learning-Based Method for Categorizing Indian Bird Species

The undertaken research describes a distinctive method for classifying the Indian bird species using deep learning models and auditory inputs. The collection of data was made possible using the xeno-canto public website. The nine Indian bird species with the most recordings were chosen for the purpose of this research. The audio stream was processed using signal processing techniques to extract cepstral, spectral, and statistical features. Using the feature selection technique, the best characteristics were picked, providing 21 characteristics. To categorize the species, a deep learning model was created using Artificial Neural Networks (ANNs) and trained with the retrieved characteristics. The accuracy of the model was assessed over the test data, and it yielded encouraging results for the 9 species classification, with an accuracy of 97.79%. The results of this study indicate that deep learning models are significant and may be used to efficiently categorize Indian bird species based on their acoustic data.
Energy efficient robust tree-based routing protocol for wireless sensor networks

The creation of compact, reasonably priced nodes with the capacity to sense, communicate, and process data is expected to be facilitated by advancements in processor, memory, and wireless technologies. Together, these nodes can function in networks to carry out distributed sensing of environmental events. Sensor webs made up of nodes with wireless connections are used to gather valuable data from the field. It is important to collect sensor data in an energy-efficient way if a sensor network is to function for an extended period. As a result, we proposed a protocol called Enhanced Tree-Based routing protocol which is self-organised where it enables the nodes to connect to other node which has highest virtual potential energy. Each node automatically connects to the next node and transmits data to the sink node.
DDoS Attack Detection Using Ensemble Machine Learning Approach

As more devices connect to the internet, security becomes a major daily challenge. The prevalence of Distributed Denial of Service (DDoS) attacks, which are highly sophisticated, is a growing concern. These attacks are increasingly difficult to detect and are causing significant disruptions. They hinder legitimate users’ access to services, resulting in decreased network performance and, in severe cases, complete network shutdown. DDoS attacks reached a record high of nearly 13 million in 2022, while adversaries are increasingly skilled at bypassing traditional DDoS mitigation and defense mechanisms. In the last four years, over 4 million DDoS attacks have persisted for over an hour, with 25% of those lasting for more than 12 hours. This emphasizes the need for adaptive DDoS solutions that can effectively handle both short and long-lived attacks. This paper proposes an ensemble machine-learning technique which outperforms standalone machine-learning algorithms.
Machine Learning Technique to Assess the Clearance of a Satellite Launch Vehicle Lift Off for a Measured Wind

Day of launch wind biasing strategy is followed for satellite launch vehicle missions to reduce loads acting on the vehicle and to minimize the control force requirements. For this purpose prevailing wind at the launch site is measured very close to launch (T-1hr) and the launch vehicle trajectory is designed considering the wind. Multiple Rigid body, Flexible body, and Monte Carlo simulations are carried out for evaluating the launch clearance for the prevailing wind and for the newly designed trajectory. This work aims to develop machine learning (ML) models, to predict a GO/NO-GO for launch by taking the Day of launch (DOL) measured wind as input. This work serves as a proof-of-concept on how ML models captures the intelligence of various mission sub-systems performance analysis. As ML models are data driven, large set of measured/ synthesized winds that inherently has the signature that lead to launch clearance or no clearance was collected /generated for the typical launch vehicle. Different ML, Deep Neural Network (DNN) and hybrid models under supervised settings have been applied on the data. This paper describes the various ML models developed and the results and its comparison.
Target Detection Improvement in OFDM-IM JCR System using POGM

In this paper, we propose an index modulated orthogonal frequency division multiplexing (OFDM-IM) based JCR system capable of generating target profiles with very high peak-to-sidelobe ratio (PSLR). The modulation symbol cancellation method used in the conventional OFDM radar systems can be used only for the active subcarriers in the OFDM-IM systems resulting in partial channel information. The proposed JCR system recovers the full channel information without using modulation symbol cancellation and signal reconstruction; therefore, it is able to produce range-velocity profiles with high PSLR. Multi-target scenarios are considered in this paper, where the target detection and parameter estimation are carried out by using the proximal optimized gradient method (POGM), which is a sparse recovery algorithm. Simulation results show that the target parameters are estimated accurately, irrespective of the number of active subcarriers used in the OFDM-IM signal.
Analytical Hierarchy Process based routing protocol for Multi-channel Wireless Sensor Networks

An Analytical Hierarchy Process (AHP) based multi-channel routing protocol for multi-channel WSN (MWSN), has been proposed. It is based on the virtual grids formed out of the whole sensor network. The considered network is a combination of static and mobile nodes; some sensor nodes are fixed and others are mobile. The sink is fixed and placed outside the sensor field. Data forwarding decision, from the source to the base station, is taken with the help of a mathematical tool known as the Analytical Hierarchy Process. Channels are assumed to be colored with unique colors so that each channel can be distinctly identified at ease. Thus in an ideal situation, the entire channel starting from the source node to the base station is expected to be of single color, however, it may not be always the case. The proposed work is simulated in Matlab 7.1. The performance of the protocol has been compared with two well-known protocols namely ABORt and DRCS. The protocol outperforms both in terms of throughput and energy efficiency. The future direction of the work has been outlined.
Software Dependability Assessment Using DevOps Metrics

Assessing software dependability is a major task of Total Management Dependability strongly motivated by the framework Total Quality Management and Lean Six Sigma methodology used by software organizations to improve performance and assuring constant collaboration with stakeholders. The most recent approach in improving software quality is DevOps, a merging of software development, software quality assurance, and software deployment and integration (IT operations). This paper addresses DevOps metrics useful to assess the software dependability. Both static and dynamic models are considered. Firstly, recent developments on software reliability, availability, safety, security, and resilience are reviewed. In the second part, extensions of dependability metrics obtained by soft computing (fuzzy, intuitionistic-fuzzy, and neutrosophic numbers) are presented along with practical examples.
Integrating Real-Time Main Controller with Power Prediction Interface and State Classifier in Energy Harvesting Systems: An Approach

Embedded systems with energy harvesting capabilities present an intriguing solution for demanding scenarios where prolonged operation is required, and replacing or replenishing the energy storage unit with a power supply is not feasible. Therefore, effective management of both consumed and generated energy becomes crucial, given the dynamic behavior of applications utilizing such systems. Based on these scenarios, the proposed approach integrates power management and saving techniques into an embedded system that utilizes energy harvesting resources. These techniques include a strategy for executing system tasks while considering energy and time constraints, as well as the utilization of a state classifier to switch the system’s overall operating mode. The approach is analyzed within a specific scenario where the embedded system functions as a sensor node, dedicated to the exploration and monitoring of oil and gas reservoirs. The results obtained from this analysis showcase the approach’s promising potential in extending the system’s lifetime while effectively managing the demands of hard real-time tasks.
Wireless Charging Technology for an Electric Vehicle

Electric vehicle charging is now feasible without the need for a cable, technology known as wireless charging. A receiver coil mounted on the underside of a vehicle receives electricity by electromagnetic induction from a transmitter coil buried beneath the ground. Static, dynamic, and quasi-dynamic wireless charging systems are the three types available for electric vehicles, each of which is best suited for a particular use. A stationary wireless charging method charges an electric vehicle when it is parked over a charging pad. Electric vehicles that are charged remotely using a semi-dynamic system move slowly across a series of charging pads. Dynamic remote charging framework for electric vehicles charges the vehicle when it is moving at high velocity over a nonstop charging strip implanted in the street. In comparison to conventional plug-in charging, a electric vehicle wireless charging technology offers a number of advantages, including convenience, safety, dependability, and efficiency. However, it also faces a number of obstacles, including standardization, low power transfer, electromagnetic interference, and high cost.
Rat Swarm Optimization and Machine Learning Algorithms for Accurate Leukemia Diagnosis via Gene Expression Analysis

Cancer can be caused by a sharp rise in the number of cells that might damage internal organs. Leukemia is a form of cancer that affects the body's blood-forming tissues, including the lymphatic system and bone marrow. Recent technical developments have made it possible to quickly assess the cellular makeup of various cancer forms. Each properly positioned tiny dot on a microscope slide printed with a DNA microarray contains either a DNA sequence or a gene. This study aims to enhance machine learning algorithms like Passive Aggressive Algorithm, Ridge Classifier, Quadratic Discriminant Analysis, XG Boost Algorithm, Stochastic Gradient Descent algorithm, K Nearest Neighbour Classifier, Random Forest Classifier, and Perceptron Model by using Rat Swarm Optimization as a transform. In this analysis, gene expression data from 282 patients with leukemia was taken into account. To reduce the number of dimensions in a dataset, Principal Component Analysis (PCA) is used. Importantly, Ridge Classifier delivers the greatest Balanced Accuracy score of 94.92% for Leukemia cancer data when combined with Rat Swarm Optimization.
Wearable Substrate Integrated Waveguide (SIW) Iris Band Pass Filter (BPF) over Jeans Substrate

This paper explores the designing technique of body wearable iris band pass filters over substrate integrated waveguide (SIW) by opting jeans as a substrate material with relative permittivity of 2.72, thickness 2mm and dielectric loss tangent of 0.002. Simple microstrip to SIW feeding technique is used for impedance transition. A number of iris configurations are studied and presented with simulated outcomes. All configurations are compared and presented with vital filter characteristics. The filters have been developed targeting body wearable trans-receiving systems in microwave X-bands in terrains where installation of bulky components produces transport challenges. All simulations are carried over Ansoft HFSSTM.
A Compact Tri-band Microstrip Patch Antenna Design for 5G millimeter wave applications

This paper outlines a unique and effective technique for creating a miniaturized three-band microstrip patch antenna that can be used in 5G millimeter wave wireless systems. The proposed antenna operates in three distinct bands at 15.2GHz, 23GHz, and 29.8GHz. The Roger RT/Duroid 5880 (tm) material has a permittivity (εr = 2.2) and a thickness (Hs) of 0.8mm, and the antenna was designed based on these attributes. This compact antenna utilizes a hexagon shape patch with three rectangular slots to enhance antenna performance. The obtained results indicate that the proposed antenna demonstrates a better return loss for all three frequencies are 15.2GHz, 23GHz, and 29.8GHz respectively. The antenna has a measured gains are 7.183dB at 15GHz, 5.79dB at 23GHz, and 6.72dB at 29.8GHz. The antenna directivity and radiation patterns are further investigated to determine the antenna's behavior. Simulation carried out using Ansys HFSS reveals an overall radiation efficiency of more than 95%. The proposed antenna is believed to have strong potential for 5G applications, mainly because of its small size, high gain, excellent radiation efficiency, and tri-band design. Overall, this antenna design offers potential advantages in size, weight, fabrication cost and efficiency to be used in 5G millimeter-wave applications.
An Expert System for Facial Micro-Expressions Based Lie Detection

For the purpose of assisting authorities like the police and attorneys in court, lie detection in humans is an important topic of research. Although there has been a lot of research in this area, the polygraph remains the most often utilized method. The development of technology has made it possible to record minute facial expressions and nonverbal clues that are difficult to see with the unaided eye. In this study, we seek to identify specific facial cues that could aid in the development of a reliable and effective lie-detection project. To choose the most effective strategy, we will evaluate our facial expression and micro-expression performance metrics. The results are Based on the Duchenne smile and the connection between lying and an increase in blink rate. This paper explores the applications of OpenCV to lie detection.
Modelling an In Band full duplex transceiver through Analog Self interference Cancellation for 5G System designs

In-band full-duplex (IBFD) technology is critical to 5G and beyond due to its potential to increase spectrum utilization and data transfer rate. ADC receiver saturation because of substantial self-interference (SI) is one of the key challenges with full-duplex radios. To reach their maximal benefit, a large SI must be suppressed until it reaches the noise floor. Researchers have suggested passive and active suppression as two different types of analogue self-interference cancellation (SIC) approaches to address this issue. In the latter scenario, the tuneable RF element—like a multi-tap circuit—replicates and subtracts the SI. Following a description of the working principle and implementation details of a multi-tap adaptive filter, an analogue RF SI canceller system architecture is proposed. The canceller operates in conjunction with a circulator and makes a copy of the sent signal using an analogue RF circuit with variable attenuators, delay, and a 180-degree phase shifter. This copy is then added to the desired received signal. In MATLAB/Simulink, the canceller has been modelled and simulated. Two test signals (‘SI’ and ‘SOI’ signal) were developed to design the proposed system. Finally, simulation outcomes are shown to evaluate the overall performance of the SIC. Over a 20 MHz bandwidth, the suggested canceller achieves -51.0824 dB of SIC with a 2-tap configuration. To obtain high cancellation, SIC requires the use of numerous approaches. Among these, analogue SIC achieves the first and most effective mitigation. The key challenge in analogue SIC is designing a circuit and manually tuning the adaptive filter to provide the best canceller output.
Prediction of Vehicle Status and Alerting System

An increase in automobile vehicle also increases the job of predicting the vehicle status . Predicting the vehicle status includes predicting the accidental smoke and LPG gas and also monitor them by give audible and visible alerts . Another most important Adxl335 is used when unnecessary vibrations and tilts in the vehicle.This projects aims to have safety for the vehicle and passengers. Model includes the GPS tracker through this we can get the location and GSM is used to send the relevant vehicle location values as a SMS . So by this the guardians or else the vehicle owner immediately gets the message and would take an action. We may use this technique to decrease lengthen the life of our vehicles. This project intends to develop an vehicle tracking. The components of this tracking system are a GPS receiver, The user's mobile device will receive the position information. GAS, SMOKE, and ADXL335 sensors in this system are used to detect status of the vehicle. The esp8266 Wi-Fi module used to upload sensor data, together with the date and time, to the Thingspeak cloud. If there is any abnormality, a buzzer will sound an audible warning. In the event of an accident, the vehicle will use GSM and GPS to send an alert SMS to the predetermined mobile number along with the location. We created a system called an IoT-based vehicle monitoring system that allows us to easily monitor all vehicles in order to monitor them all. In this project, the Internet of Things is crucial. The amount of smoke and gases is monitored by exhaust sensors, and with the help of IOT, the value is updated in the cloud. This makes it simple for the owner of the car and the transport agency and an individual to keep an eye on it.