Miracle-dz/UniFAR-FTUs huggingface dataset:
Educational Support for SMEs Transitioning from Industry 4.0 to Industry 5.0 - Insights and Lessons Learned from European Cooperation Projects

The ongoing transition from Industry 4.0 to Industry 5.0 brings promising prospects for small and medium-sized enterprises (SMEs) to bolster their competitiveness and sustainability. To fully capitalize the benefits brought by this transition, SMEs need to master the upskilling of their workforce that conforms to the rhythm of technological development. Creating an educational symbiosis which supports this upskilling prompt is therefore crucial. The interdependence between research and education institutions and SMEs plays a vital role in cultivating a culture of continuous learning. This enables SMEs to effectively tackle the hurdles posed by the ongoing shift from technology-driven competition to one centered around creativity. The authors explore the European projects undertaken in recent years, emphasizing the insights that have played a pivotal role in empowering SMEs to fully leverage the potential of technological advancements within Industry 5.0. Furthermore, observations concerning the collaboration between SMEs and research and educational institutions have also been incorporated.
Exploration of Image Segmentation: A Comprehensive Review of Traditional Segmentation Techniques

In recent years, Image processing techniques have gained increased significance across a broad range of applications concerning computer technology. The digital image is processed by applying a set of algorithms this process is called Digital Image Processing. Segmentation of an image is an essential action in the method of object identification in digital image processing. The overall image is segmented into various regions so that their information can be utilized to classify different elements in an image. The visual perception quality depends upon the type of segmentation. For tasks like recognizing and categorizing various items in an image, segmentation is essential. Its applications are vast, and its importance lies in its ability to provide precise and detailed information for a wide range of tasks. The fundamental idea of this paper is to review the notion of different digital Image segmentation techniques. The literature review provides a comprehensive overview of previous studies, highlighting their methodologies, strengths, and limitations, and serves as a foundation for the current study.
EMD-Based Feature Extraction Toward Real-Time Fear Emotion Recognition Application Using EEG

In recent years, many researchers have shown interests in EEG-based emotion recognition for the application of Brain Computer Interface devices. Therefore, this study investigates the applicability of Empirical Mode Decomposition (EMD)-based feature extraction method for real-time EEG fear emotion recognition. In this study, instead of relying on publicly available datasets such as the DEAP dataset, the EEG data are collected independently by utilizing video clips available on the Internet to elicit fearful emotions. The algorithm mainly consists of two parts: feature extraction and fear emotion recognition. In the feature extraction stage, the acquired EEG signals are divided into five seconds segments and decomposed into several Intrinsic Mode Functions (IMFs) using EMD. Subsequently, the mean and Differential Entropy are extracted from the first five IMFs. These features are then classified by Support Vector Machine. To investigate the applicability of EMD, the EMD-based feature extraction method is compared to conventional methods, namely Short-time Fourier Transform and Wavelet Transform. As a result, the EMD-based method has demonstrated superior accuracy in both subject-dependent and subject-independent classification compared to the other two methods.
Prismatic Joint Mechanism for Wheel-Legged Locomotion with Pushing Force Enhancement

This paper proposes a prismatic joint mechanism composed of timing belts and pulleys to enhance the wheel-legged locomotion of a mobile quad-arm robot proposed by the authors. The previous mobile quad-arm robot had four arms composed of revolute joints only, and the pushing force was insufficient to walk and lift the torso simultaneously. In this study, the third revolute joints of the two rear arms are replaced with the prismatic joints. The arm with the prismatic joint mechanism achieves a maximum force of 222 N independent of other joint angles. The maximum force is enough to lift the robot while the robot walks, in which the robot's weight is almost 18 kg. We validated the prismatic joint mechanism via a simulation and experiment and conducted four tasks: standing, lateral wheel-legged locomotion, forward wheel-legged locomotion, and object grasping with the mobile quad-arm robot.
Exploration and Reflection on Precise Recommendation in Complex Information Environments: Boundaries, Challenges, and Future Prospects

As a proactive filtering technique, recommender systems (RSs) analyze users’ prior preferences to assist them in selecting valuable information from a vast amount of data. It is a crucial tool for handling information and cognitive overload. However, with the development of digital and communication technologies and shifts in user awareness, existing RSs face new limitations and challenges, including data violations, algorithmic discrimination, information cocoons, etc., making them inadequate to meet the diverse application requirements and user information needs across various scenarios. Therefore, a more effective RS is required. This study aims to identify several present limitations and challenges faced by RSs and provide insights for the design of future RSs. Firstly, some basic concepts and techniques of RSs are introduced. Then, several significant limitations and challenges in designing an effective RS are investigated and reported in terms of data, algorithms, and evaluations. Finally, opportunities for further study are proposed in response to the problems reported.
Low-Resolution Face Recognition(LRFR) System: A Review

Over the last few years, low-resolution face recognition (LRFR) has received a lot of attention. This kind of system comes into assistance in the recognition of the correct identity of a person whose image is taken in low resolution but whose image is stored in a high-resolution image database, as in real-world situations, high-resolution or high-quality photographs may be difficult or impossible to acquire. Video surveillance is one of the most important prospective application fields for LRFR systems. The video sequences captured by surveillance cameras will need to be analyzed automatically as the number of cameras increases, particularly in urban areas. This work presents the challenges of that face recognition system (FRS) and an in-depth review of the dataset used for training and testing the LRFR system and the method used in the LRFR system.
U-Net Based Brain Tumour Segmentation

In recent years, a large amount of medical datasets were introduced to the public domain with the goal of paving the way for innovative and advanced methodologies to tackle various health issues. The Brain Tumour Segmentation (BraTS) challenge provided multimodal clinical information for the purpose of segmenting brain tumours. This task plays an integral role in accurate diagnosis and planning of treatment of patients. Over the years, various models and techniques have been proposed during every rendition of the BraTS challenge. This paper presents a comprehensive study on the BraTS’20 dataset and the state-of-the-art model which has stood the test of time, the U-Net. Its modifications are evaluated to ascertain which provides the most accurate segmentation of tumours.
Transistor Size Optimized Ultra-low Power NNPT and PNPT Level Converter Designs

Ultra-low power applications viz. energy harvesting, biomedical signal processing, and wireless sensor nodes require sub-threshold or near-threshold circuits with level converter designs which are not only energy-efficient but also resilient to process variation. In this work, we have considered two single threshold 5 transistor (5T) level converters in 22 nm technology and modified the critical nmos and pmos transistors. Such modified architectures of level converters consume ultra-low power consumption and significantly less silicon area. Furthermore, we have carried out the worst case analysis to assess the robustness of those 5T LCs.
Real- Time Failure/Anomaly Prediction for Robot Motion Learning Based on Model Uncertainty Prediction

End-to-end robot motion generation methods using deep learning have achieved various tasks. However, due to insufficient training or the occurrence of abnormal conditions, the model sometimes fails tasks unexpectedly. If failures/anomalies can be predicted before occurring, irreversible task failures can be prevented. In this paper, we propose a method of predicting model uncertainty to predict failures/anomalies in real-time. For a naive method, we used a model that predicts the robot's actions stochastically and also tried a method that predicts failure/anomaly on the basis of the variance. However, it was experimentally shown that the variance due to the variation of the training data and the uncertainty of the model cannot be distinguished. Therefore, by predicting the likelihood of the model, which corresponds to the degree of discrepancy between the model and observations, in real-time and treating it as the uncertainty of the model, we applied it to the prediction of failure/anomaly. The method's effectiveness was demonstrated by achieving a high judgment accuracy rate of 85% (17/20 cases) in an object-picking task.
Reinforcement Learning Methods for Fixed-Wing Aircraft Control

In recent years, reinforcement learning (RL) has been widely researched for fixed-wing aircraft control, offering transformative potential for more adaptive, efficient, and autonomous flight operations. This paper presents a comprehensive analysis of the current state, advantages, challenges, and future prospects of employing RL in fixed-wing aircraft control. The advantages of RL include model-free, adaptive decision-making, and effectively solving complex nonlinear constraints, leading to increased efficiency and maneuverability in high-dynamic environments. However, significant challenges, such as the guarantee of stability and optimization, interpretability, and sim-to-real transferring, highlight areas necessitating further attention and development. Overcoming these challenges requires collaborative efforts among aviation experts, control theory experts, and machine learning researchers to ensure safe, reliable integration of RL in flight systems. As research and advancements continue, the RL holds promise for providing a more efficient, maneuverable, and autonomous strategy for controlling fixed-wing aircraft in the future.
An Adaptive Neuro Fuzzy to Predict Cryptocurrency Based on the Crisp Method: Case of COVID-19

Over the past few years, there has been a notable surge in interest towards cryptocurrency, especially in the context of the crisis, where researchers have been diligently examining the influence of the coronavirus on cryptocurrency returns.Numerous studies have utilized econometric and machine learning techniques to forecast cryptocurrency prices, but most of them have focused solely on the financial domain. This paper introduces a novel approach called ANFPC (Adaptive Neuro Fuzzy Prediction Cryptocurrency), which combines insights from both the financial and health domains to predict the price fluctuations of various cryptocurrencies such as bitcoin, ethereum, cardano, xpr, and dogecoin. The approach relies on the ANFIS Model, a fusion of fuzzy logic and artificial neural network (ANN). The experimental findings demonstrate that ANFPC provides accurate predictions, as measured by metrics like Mae and Mse, outperforming traditional ANN and LSTM methods. This approach proves to be a valuable decision support tool for data analysts in the realm of cryptocurrency prediction.
Inspection Techniques Using Scanning Acoustic Microscopy for Silver Sintering Applications in Power Electronic Modules

This work presents a practical perspective to Scanning Acoustic Microscopy (SAM) for Ag-sintering applications in power electronic modules and devices. It aims to present typical challenges to consider whilst performing C-SAM for inspecting and analysing microstructures within power electronic devices, with emphasis on silver-sintered interconnects. The study presents a vast number of acoustic images (discrete devices in TO-247 packages, power modules, and also custom samples of substrates and devices), where several defects are identified, including voids, cracks, and delamination. Challenges for Ag-sintered layers inspection related to advanced surface finishes are also revealed and discussed in detail. The suitability of C-SAM for inspecting small and large areas of Ag-sintered interfaces is discussed, and showcased with custom samples, and a commercial power module.
Monitoring vegetation systems in the great plains with ERTS

The Great Plains Corridor rangeland project utilizes natural vegetation systems as phenological indicators of seasonal development and climatic effects upon regional growth conditions. A method has been developed for quantitative measurement of vegetation conditions over broad regions using ERTS-1 MSS data. Radiance values recorded in ERTS-1 spectral bands 5 and 7, corrected for sun angle, are used to compute a band ratio parameter which is shown to be correlated with aboveground green biomass on rangelands.
Genetic Algorithm Based Feature Selection and Optimized Edge Detection for Brain Tumor Detection

This paper presents a novel approach for brain tumor detection using Steady-State Genetic Algorithms for filter optimization and feature selection. The proposed method utilizes two genetic algorithms to select relevant features and optimize filter coefficients. A genetic algorithm is employed to select a subset of features using random forest as the fitness evaluator. Experimental results demonstrate the effectiveness of the method in accurately detecting brain tumors with reduced computational complexity. Subsequently, another genetic algorithm is employed in which the initial population of fractional order filters is evaluated using ideal edge images, and the top-performing filters undergo crossover and mutation operations. After several generations, the filters with the highest fitness values are selected as optimized filter coefficients. The integration of genetic algorithms with brain tumor detection shows promise for optimizing filters and selecting informative features, improving clinical decision-making.
Streamlining Drone Battery Charging: The Power of Wireless Transmission and Embedded Automation

This project introduces a non-radiative wireless power transmission method for charging the batteries of drones, specifically unmanned aerial vehicles (UAVs). It involves the development of an advanced drone battery charging system consisting of a charging station equipped with wireless power transmitters and a receiver for drone battery charging. The system utilizes a resonance inductive coupling-based wireless power transmission technique. While the efficiency of power transmission in inductive coupling is reliant on a strong coupling between the transmitter and receiver, drones often struggle to land accurately on charging stations or devices to achieve maximum coupling for efficient wireless power transfer. To align the transmitter and the receiver for the coupling without loss, infrared sensors and the ultrasonic sensors are used and the power is transmitted. However, the proposed system is a novel approach based on the hill Embedded Automation system to regulate the coupling without the need for physical sensors.
Cardiac Disease Prognostication System

Cardiovascular (cardiac) disease is defined as a certain range of conditions and problems affecting the heart. At present, most health problems are due to heart disease. People may lead to death due to this disease by having some of the symptoms like angina (chest pain), hypertension, cholesterin, sugar, etc. Heart disease prediction is very much complicated. In this Data Science plays a significant act in the medical field. ML is a part of data science that produces a result with the correctness of the presence of heart disease for many patients. This can be very much helpful for medical researchers and doctors all over the world. Machine Learning is the sub-field of Data Mining. This paper uses Some Machine algorithms like Random Forest (RF), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree (DT), Linear Regression, Naïve Bayes (NB), Logistic Regression, etc., in that the particular algorithm Random Forest has been given high accuracy and which can identify whether the cardiovascular disease is present or not for the patients. This can also be trained and validating the data for finding CVD. The dataset for this paper is collected from the UCI Repository it consists of four datasets for training and testing. This paper aims to create a web app for both doctors as well as for users (public).
Urban Data Management using Cloud Computing and IoT

Considering quick growth of technical knowledge, it is pertinent for the metropolitan stake-holders to improve the urban living standards by implementing effective policies assistance from data analysis using the Internet of Things (IoT). The IoT involves usage of various sensors for recording data that corresponds to different aspects of urban life, such as health, transport and governance. The interconnection of these sensors enables data analysts and governing bodies to decipher patterns in data, and in turn better understand the urban environments for efficient city planning, eventually creating smart cities. The design and implementation of smart cities is a hot topic nowadays in the field of urban development. This paper reviews the modern technologies that are being used to implement the smart city concept, in turn making it sustainable and more environment-friendly. The necessity of having a variety of sensing platforms for sustainable urban development is discussed. There is a discussion on different data collection and processing frameworks being deployed in smart cities, highlighting the key benefits of utilizing modern technologies like big data, cloud computing, machine learning (ML) and artificial intelligence (AI) with the IoT. The challenges encountered during these processes are also examined, which create new research opportunities. This paper will serve as a guideline for researchers working in the field of sustainable urban development using IoT along with the other modern technologies.
Attention-Guided Residual Network for Skin Lesion Classification Using Deep Reinforcement Learning

The increasing incidence of skin cancers emphasizes the critical need for early detection, making effective therapeutic intervention possible. Although existing skin lesion segmentation methodologies offer some solutions, they are often hindered by inconsistent annotations present in the datasets. Accurate delineation of lesion boundaries is fundamental for diagnosing various skin conditions. In this research, a skin lesion segmentation method is introduced using an attention-guided reinforcement learning algorithm, framed within the context of a Markov Decision Process. This approach mirrors the method doctors use when identifying areas of concern on the skin. To improve classification accuracy, a self-attention mechanism is integrated into the network. This addition refines the focus on vital regions within the images, ensuring more precise segmentation. With deep reinforcement learning, the agent is trained to segment these regions, employing a series of actions that are continuously variable. This continuous action space is exploited by the segmentation model, which utilizes the deep deterministic policy gradient algorithm, allowing for iterative refinement from broader segmentation to detailed delineation. The proposed method is tested on benchmark dataset, ISIC 2017, and achieved an accuracy of 97.10% for naevus, 96.33% for melanoma, and 95.13% for seborrheic keratosis cases. Additionally, the enhanced approach with attention module, outperformed several contemporary skin lesion segmentation techniques in terms of performance.
Neural Style Transfer Based Infrared-Visible Fusion

Infrared-Visible image fusion is a method of integrating information from images captured in separate spectra, including infrared (IR) and visible light. This technique aims to create a single composite image that takes advantage of the strengths of the original image while minimizing their inherent limitations. This study introduces a Neural Style Transfer based non-End-to-End framework for seamlessly merging infrared and visible images. Proposed approach entails an optimization process where fused features interplay with the initial composite image. Then, the vital features are extracted from input images using the first four layers of the ResNet50 network. These features subsequently unite through an appropriate fusion rule. The original images are blended using the average rule to formulate the initial composite image. By employing backpropagation, the final synthesized image emerges as the initial composite image is fine-tuned with the imbibed features. In this study, the efficacy of proposed fusion framework is validated by conducting experiments on the TNO Image Fusion dataset. The outcomes of these experiments clearly demonstrate that our approach outperforms currently approaches, as evident from improvements in both subjective and objective assessments.
Cyber Security and Dual Hash Mechanism for Cloud-Based Internet of Transportation System

The advent of cloud computing and the Internet of Things (IoT) has brought about significant advancements in the field of transference systems. The combination of cloud-based services and the IoT has given rise to the concept of the Internet of Transportation Systems (IoTS). However, with this integration comes the need for robust cyber security measures to protect the sensitive data and ensure the safe operation of these systems. Cyber security play crucial roles in ensuring the safety and reliability of cloud-based Internet of Transportation Systems (IoTS). IoTS refers to the integration of several transportation modules, such as vehicles, infrastructure, and communication systems, through the use of the internet and cloud computing. This research work proposed a cryptographic construction as HMAC (Hash-based Message Authentication Code) to combine dual hash values. A dual hash value which refers to the use of two separate hash functions to compute hash values for a given input. Using an input, hash functions are computational algorithms that generate a fixed-size outputs known as a hash value or hash code.
Active Search and Relocalization Framework for Position-Lost Recovery Under Limited Number of AMRs

Autonomous mobile robots are required to operate continuously in uncertain environments without human intervention. Addressing the self-localization failure problem, this paper proposes an efficient re-localization method for multiple autonomous mobile robot systems that utilizes lost-robot searching actions by surrounding robots, based on a rough estimation of the lost-robot's positional area. This approach ensures swift re-localization through active movements, effectively countering occlusion challenges inherent to cooperative localization. The system implemented functions for lost detection, searching, relative position recognition and information sharing. Real-world experiments in a 5 x 6 m indoor environment demonstrate the effectiveness of our approach. Re-localization is successfully achieved within 0.5 m of localization error in all 20 tests in an environment of occlusion between two robots.