Miracle-dz/UniFAR-FTUs huggingface dataset:
Stakeholder Needs in Systems Engineering: A Proposal for a Formal Definition

Stakeholder needs are the basis for the derivation of technical requirements in systems engineering. However, current definitions of stakeholder needs do not clearly distinguish stakeholder needs from requirements and do not provide a clear characterization. A clear distinction would especially allow the relationship of needs and requirements to be analyzed in the model of SGE - System Generation Engineering. The aim this contribution is the definition and formulation of stakeholder needs in systems engineering, as well as their description in the model of SGE. For this purpose, a systematic literature review is conducted to examine how stakeholder needs are described literature. The definition proposed based on these results is then evaluated in two industrial studies. A case study at a German vehicle manufacturer has shown that the characteristics of the definition can be found in practice and a study at a German industrial machine manufacturer has shown that the definition can help product developers to identify stakeholder needs.
Detecting Anomalies for corn crop disease using Isolation forest

Today, the most challenging issue for researchers is to continue their research for crop disease using deep learning and image processing. One of the crucial detection methods used in one-class classification to find the outliers in the dataset is anomaly detection. The isolation forest approach is used in this work to discover anomalies using the corn dataset from Kaggleas a benchmark dataset. To find abnormalities in the provided dataset, this algorithm mimics a binary decision tree technique. This algorithm’s classification accuracy is 89 percent. For the corn dataset, the experimental findings of the isolation forest show improved outcomes. Other anomaly identification methods will eventually combine anomaly scores and new datasets on leaf disease.
Sensor Placement Optimization using Random Sample Consensus for Best Views Estimation

The estimation of a 3D sensor constellation for maximizing the observable surface area percentage of a given set of target objects is a challenging and combinatorial explosive problem that has a wide range of applications for perception tasks that may require gathering sensor information from multiple views due to environment occlusions. To tackle this problem, the Gazebo simulator was configured for accurately modeling 8 types of depth cameras with different hardware characteristics, such as image resolution, field of view, range of measurements and acquisition rate. Later on, several populations of depth sensors were deployed within 4 different testing environments targeting object recognition and bin picking applications with increasing level of occlusions and geometry complexity. The sensor populations were either uniformly or randomly inserted on a set of regions of interest in which useful sensor data could be retrieved and in which the real sensors could be installed or moved by a robotic arm. The proposed approach of using fusion of 3D point clouds from multiple sensors using color segmentation and voxel grid merging for fast surface area coverage computation, coupled with a random sample consensus algorithm for best views estimation, managed to quickly estimate useful sensor constellations for maximizing the observable surface area of a set of target objects, making it suitable to be used for deciding the type and spatial disposition of sensors and also guide movable 3D cameras for avoiding environment occlusions.
Cloud Based Smart Medical Recommendation System Using Pattern Recognition

To build smart medical recommendation system the data needs to be collected from laboratories. The system is intended to provide services as control and manage data samples, industry standards, real test results, reports, human resources in the laboratories, machines, tiny instruments, and automation of the workflow. Combining the data from the laboratories and medical institutions enable ease of communication without time delay. The challenging task is that identifying appropriate hardware and software components which provides compatibility in collecting data and their processing steps, repository storage, secured distribution, and the policies used for information representation in the results. In order to automate the entire process of medical recommendation system the metric parameters are used for determining the efficiency and productivity of the lab to enhance the quality of control systems. The vulnerability lies in maintaining traditional way of records in paper and error in the data. The purpose of this project is to develop the process of daily tasks will be computed through the database using pattern recognition techniques.
Location Monitoring System for Sailboats by GPS Using GSM/GPRS Technology

A monitoring and locating system for sailboats is developed using the general packet radio service/global system for mobile communications (GPRS/GSM) and global positioning system (GPS) technology. The implemented prototype can track the location of sailboats that do not go out more than 10 km into the sea. The system uses a GPS module to obtain the boat’s coordinates; it has a sensing station: environmental temperature, humidity, barometric pressure, and the angular movement of the ship. A structured query language (SQL) database has been implemented to store data received via AVR-GPRS. System monitoring tests were carried out over a 24-km route onboard a sailboat, obtaining its location during the entire course along the coast of the Atlantic Ocean, Gulf of Mexico.
Poster: Improving Spectrum Based Fault Localization For Python Programs Using Weighted Code Elements

In this paper, we present an approach for improving Spectrum-Based Fault Localization (SBFL) by integrating static and dynamic information about code elements. This is achieved by giving more importance to code elements that include mathematical operators compared to other types of elements (e.g., declaration, selection, iteration, or function call) and appear in failed tests. The intuition is that these elements are more likely to have bugs than others. The proposed approach is applicable to any SBFL formula without requiring any modifications to their structures because the weighting is done on the ranking list and not on the formulas. The experimental results of a preliminary study show that our approach achieved a much better performance in terms of average ranking compared to the underlying SBFL formulas. It also improved the Top-N categories; it doubled the number of cases in which the faulty method became the top-ranked element, and in all cases the fault became part of Top-5 of the ranking list.
Obtain Better Accuracy Using Music Genre Classification Systemon GTZAN Dataset

The term “music genre” refers to any method of categorization that divides musical compositions (or even individual musical phrases) into distinct musical forms or styles. Religious music, secular music, classical music, and pop music are only few of the many musical genres that exist. The amount of data available to us is increasing rapidly, making it infeasible for manual curation. In this work, we apply a variety of simple and basic machine learning algorithms namely Logistic Regression, K-Nearest Neighbor, Random Forest, Support Vector Machine and Artificial Neural Network, along with dimensionality reduction techniques namely, PCA, KPCA and LDA, on the GTZAN dataset. Further, we compared their accuracies and found that the combination model of KNN with PCA provides the highest accuracy of 77.41% among the compared models.
Pre-Processing Investigation For Brain Abnormality Detection And Analysis Through MRI of Brain

Both men and women face equal risk from conditions such brain tumours, strokes, blood clots, and multiple sclerosis (MS). The most frequent and prevalent of these brain illnesses is a brain tumour. The prognosis and therapy for a brain lesion depend on how quickly and precisely it is diagnosed. However, neuroradiology experts are the only ones qualified to make the diagnosis, as it is extremely difficult. Each medical report from an imaging investigation must be reviewed and confirmed by at least two physicians. If there's a problem, it could mean a biopsy or even surgery. This study describes a fully automated procedure for removing artefacts and the skull from magnetic resonance (MR) pictures of the brain by utilising computational geometry, wavelet decomposition, and thresholding. The intermediate step of correct preprocessing of MR images is thresholding using the standard deviation method. For various MR images, the suggested preprocessing approach achieves a high degree of accuracy with a small error rate. This preliminary processing is employed to lessen the likelihood of erroneous detection. As a result, the accuracy with which diseases are diagnosed using brain MRI by a computer system is improved.
Grid Interactive Solar PV-fed BLDC Motor Using Predictive Current Control Approach

A grid interactive solar PV-fed BLDC motor using non-linear based model predictive current controller is implemented at PV side DC-DC converter and grid side single phase voltage source inverter (SPVSI). For this application, linear based propotional integral (PI) control structures are used in several articles which are designed at single operating point. In most of the literature, authors have not been considered when DC loads are present and their changes occur suddenly, when irradiation suddenly absent or available or changes time to time, when water pump is suddenly present during the operation, and when grid is absent and there is no sufficient PV power. For all this cases, the controller should react very fast to achieve better dynamics of the system. So, grid connected solar PV-fed BLDC motor with non-linear based controller is required. The proposed system achieves (i) Better dynamics during the changes in system, (ii) Unity power factor at grid side, (iii) PV array is operating at its maximum power point (MPP) mode at all the conditions, (iv) Able to supply the power to water pump in most of the cases. Finally, the overall system with controller structure is validated through MATLAB/SIMULINK results.
Adaptive Lower-Limb Prosthetic Control: Towards Personalized Intent Recognition & Context Estimation

Historical advancements in lower-limb prostheses have reflected the challenges of diverse anthropomorphic biomechanics, limiting intelligent control systems from being implemented and reflecting true user intent. With recent advancements in machine learning (ML), however, this notion is being challenged. In transfemoral-powered prostheses, time series information has been used to infer context (slope angle and walking speed) and intent (ambulation mode) and scale torque assistance accordingly in real time. In this study, we build off this work by proposing and validating a real-time framework for adaptive walking speed context estimation. Our system makes use of the general similarity in human gait patterns and iterates subject-independent ML models used for prediction towards subject-dependent models by method of batched retrospective labeling and retraining. Offline validation for walking speed estimation has been completed using seven amputee subjects' data, showing an average subject-independent MAE of 0.063 being reduced to 0.043 m/s, a 31.7% improvement. In addition, we discuss and present preliminary results for walking speed estimation and several alternative methods of retrospective labeling.
LiDAR-Based Augmented Reality for the Development of Test Scenarios on Safety for Autonomous Operation of a Shunting Locomotive

As public interest in autonomous driving systems grows, safety is becoming a critical issue. Extensive testing is therefore required before these systems can be deployed in real-world traffic. Simulation-based testing has proven to be a valuable tool for evaluating autonomous systems, but there remains a gap between simulation and real-world testing. A system may be well tested in simulation, but in real-world testing it may encounter sudden events that result in unpredictable behavior, and equipment may be damaged in the event of a malfunction. This paper describes a novel augmentation interface for Light Detection And Ranging (LiDAR) sensor data that aims to bridge this gap. The interface is designed to generate realistic test scenarios on live data streams, making it ideal for investigating special and borderline cases. The interface has been developed for the Robot Operating System (ROS) using the Point Cloud Library and is intended to be used for testing an autonomous shunting locomotive. With this interface, a data stream from a 3D LiDAR can be augmented with any given object represented by a point cloud, allowing for the use of data from both simulation and real-world environments.
Automated Shopping Cart: Reducing Long Queues One Cart At A Time

Everyone has experienced waiting in long queues to get their items billed at a shopping cart. With this product, the proposed research work intends to reduce the time spent by shoppers in queues. Our automated shopping cart can reduce the time and manual labor required for weighing and billing the items a user wishes to buy. This article has developed a shopping cart with weight sensors built into the cart to measure the load of the product, and an android application with deep learning models(MobileNetV2 and EfficientNetV2) to classify the required item. The models were trained on a custom-built dataset with 51 classes. The mobile phone camera is used to capture an image of the product. The models are used to classify this item. Once the item is classified, the weight of the item is obtained from the load cells. A database analysis is performed to obtain the price of the item. With these details, the bill is generated as soon as the user is done with shopping without waiting in long queues.
A Review of Breast Cancer Detection Using the Internet of Things and Machine Learning

Using the Internet of Things (IoT) and Machine Learning (ML), it is possible to find breast cancer quickly and accurately, which is important for getting better and getting treatment right away. In recent years, the IoT has changed in a way that makes it possible to use artificial intelligence (AI) and ML to look at both real-time and historical data. Many imaging methods, such as mammography, thermography, CT, ultrasonography, histology, etc., could find breast cancers in their earliest stages. Medical imaging studies are now heavily reliant on machine learning (ML). With the advancement of machine learning, computer-aided diagnostic (CAD) systems are becoming increasingly smarter and more capable of operating independently. The Internet of Medical Things (IoMT) is a novel method of connecting systems and apps to healthcare services and data sources for monitoring and tracking patients. It can completely change how hospitals work to find and predict cancers with higher care and customer satisfaction. The analysis found that the SVM method had the highest accuracy (98.1%) compared to the other methods. The purpose of this study is to explore the application of imaging and machine learning in conjunction with internet-of-things systems in the detection of breast cancer at an early stage.
Driver Drowsiness Classification using Machine Learning and Heart Rate Variability

Drowsiness at the wheel continues to be one of the causes of road accidents with higher fatality rates and current solutions fail to mitigate the problem from a preventive viewpoint. This work contributes towards the development of a non-intrusive and low-cost system for drowsiness detection and prediction, using a wearable device and artificial intelligence for continuous and real-time monitoring and alert of the driver’s state. In particular, Machine Learning (ML) algorithms were used to classify drowsiness levels using Heart Rate Variability (HRV) data extracted from physiological signals collected during driving simulations, following data labeling considering three different sets of drowsiness levels’ distribution. Obtained results showed promising potential, with the Extra Trees, Random Forest and K-Nearest Neighbors (KNN) classifiers achieving overall good performance, with a relative capability of distinguishing between drowsiness levels and between alert and drowsy states. However, further analysis should be made to improve the performance of the models and deal with the existing data imbalance, as well as identify the best subset of features capable of detecting drowsiness. Ultimately, it was shown that drowsiness detection using physiological data from a standalone wrist-worn wearable device in combination with ML techniques is feasible. Henceforth, prediction and forecasting of the drowsiness state should be addressed, to achieve the preventive component of the system.
A light weight Deep Convolutional Neural network model for plant disease identification

Identification of various plant diseases often prove to be a challenging task, mainly due to the extremely vast domain of diseases available in images of leaves and it’s processing techniques. This issue can be resolved by the fast-emerging machine learning concept of Networks. This research paper proposes a rather efficient and novel neural network model can make use of modern developments to create a reliable and accurate solution. Simultaneously, the designed model reduces training time and computational expense by addressing the issue of a large number of parameters. The designed neural network model trained on the PlantVillage dataset contains 11993 images with 11 different categories of classes. The model uses depth-wise separable convolutional layers, inception modules, and global average pooling to achieve the desired results. Based on the precision, recall, and Fl measure, the model scored 97.73% accuracy. The results demonstrate the model can be used effectively for recognizing disease in various plants.
An Evaluation Platform for Catheter Ablation Navigation

Cardiac arrhythmia refers to a condition of an abnormal or irregular heartbeat, which usually results in disturbed blood flow. This can lead to a reduced cardiac output and an increased risk for blood clot formation, which can cause life-threatening heart failure or stroke. Radio-frequency catheter ablation is becoming the treatment of choice for most cardiac arrhythmias. To recover the normal heartbeat, the abnormal excitation sites inside the heart are first identified and then isolated or destroyed by applying radio-frequency energy through the use of an ablation catheter. The design and precise navigation of these ablation catheters is currently a topic of considerable interest. However, the lack of a standardized evaluation setup for catheter ablation makes it challenging to properly compare different systems. In this paper, we present an evaluation platform to tackle this problem. The setup consists of a 3D printed anatomical model, a tracking system for the catheter and a dedicated graphical user interface. The performance of a catheter ablation system can then be assessed based on various performance metrics, such as the procedure duration, contact stability or the ablation angle. Finally, we conduct a proof-of-concept study to demonstrate the usefulness of the proposed setup.
The Importance of Cybersecurity in Supply Chain

Supply chain management involves the coordination of goods, services, and information from suppliers to customers, making it vulnerable to cyber threats. Cybersecurity in supply chain management has become a critical concern for organizations of all sizes and industries. Supply chain cyberattacks can result in data breaches, financial loss, reputational damage, and operational disruptions. This research paper aims to identify the cybersecurity risks and challenges in supply chain management and propose best practices for managing cybersecurity risks.
Development of Robot-assisted Ultrasound System for Fetoscopic Tracking in Twin to Twin Transfusion Syndrome Surgery

Twin to Twin Transfusion Syndrome (TTTS) is one of the most severe conditions of congenital anomaly. Fetoscopic Laser Photocoagulation (FLP) has been shown as the preferred treatment for TTTS. The complicated intrauterine environment and limited incision diameter make FLP a very challenging surgery. Besides, the motion of the fetoscope is complex. Ultrasound (US) probe is used to offer a view of the anatomy as well as the relative pose of the fetoscope. However, keeping the fetoscope tip (FT) in view is difficult. A robotic US system could potentially solve the complex US probe handling and could improve the surgeon's view of the intrauterine scene. In this paper, an automatic fetoscope tracking algorithm was developed and ensured safe interactions between robot and fragile tissues for FLP surgery. The US probe attached to the robot was controlled by a hybrid position-force control strategy. For evaluation, four fetoscope motion profiles were investigated on a custom-designed phantom. The system achieved a 78.87% visibility rate for the US image evaluation. The contact force was 0.99± 0.68 N during the tracking to ensure the patient's safety. The proposed system demonstrates the capability of accurate real-time fetoscope tracking.
Observations of strong ocean current events offshore Pemba, Northern Mozambique

ABSTRACT Three years (2008–2011) of current measurements outside Pemba (∼13°S), Mozambique, are presented. Oil and gas reserves are found in the region, and knowledge of the ocean current climate is important for safe offshore operations. The full-depth current profile at Pemba was estimated by comparing with moorings further south. The moored record was also compared with geostrophic velocities obtained from sea surface height (SSH) fields, current profiles from ship-mounted Acoustic Doppler Current Profiler and surface currents from seven drifters passing the mooring. Velocities up to 60 cm/s were observed at 850 m depth. Altimetry-derived currents, while strong (often > 100 cm/s), are probably underestimates due to coarse resolution. Direct observations show surface currents up to 170 cm/s. Four strong current events were studied in detail. Three of the four events appeared to be associated with anticyclonic eddies nearby. The time series of SSH-derived geostrophic current always showed southward velocities, never below 20 cm/s. These findings indicate that currents off Pemba may display characteristics of a western boundary current rather than being completely dominated by eddies. Strong current events are probably caused by anticyclones interacting with the boundary current and speeds on the order of 200 cm/s can be expected.
Comparison of Parallel and Serial Execution of Shortest Path Algorithms

Shortest Path Algorithms are an important set of algorithms in today’s world. It has many applications like Traffic Consultation, Route Finding, and Network Design. It is essential for these applications to be fast and efficient as they mostly require real-time execution. Sequential execution of shortest path algorithms for large graphs with many nodes is time-consuming. On the other hand, parallel execution can make these applications faster. In this paper, three popular shortest path algorithms - Dijkstra, Bellman-Ford, and Floyd Warshall - are both implemented as serial and parallel programs and tested on various problem sizes. The performance of these algorithms is evaluated by comparing their execution times. To achieve parallelization, the OpenMP (Open multiprocessing) framework is employed.