Miracle-dz/UniFAR-FTUs huggingface dataset:
Sensor Node Fault Detection in Wireless Sensor Networks Utilizing Node-Level Diagnostics

Sensor node faults are a serious threat to wireless sensor networks. They can cause node crashes or lead to the transmission of corrupted data. Especially the latter endangers the quality of subsequent data analyses. Most related fault detection approaches consider the sensor nodes as black boxes. They neglect vital information available on the node level. Consequently, most of these approaches can not distinguish between (i) irregular but correctly sensed data events and (ii) data corruption caused by soft faults. In contrast, our contribution integrates node-level diagnostics with the characteristics of the sensor data. We utilize this node-level diagnostic information to present our fault detection approach. Based on simulations and practical experiments, we show the correctness and efficiency of our approach. The results show that our approach offers a high fault detection rate and can differentiate between events and faults. Furthermore, it consumes a justifiably small overhead of resources and energy.
Adaptation of Loss Recovery Mechanisms for improving Scalability and Quality of Service in IoT Networks

Over the past decade, the Internet has known significant transformations in diverse sectors, such as health, transportation, and the environment. These changes are based to the TCP/IP model protocols, a four-layer architectural framework with the transport layer notably enabling Internet communication and Quality of Service management.This growth goes from the original ARPANET design to the appearance of Web 3.0, including Web 1.0 and Web 2.0. It encourages the creation of new communication structures, like the Internet of Things (IoT). IoT aims to unify all physical objects and devices in various fields under a common infrastructure in order to control them and also to know their states in real time [1]. Therefore ,IoT becomes one of the most well-known technologies in many markets with an immense growth of connected devices ranging from 30.7 trillion in 2020 to 75.4 trillion in 2025[2]. This gigantic number of things leads to many challenges and problems related particularly to QoS and scalability, which represent a hindrance for IoT and specifically M2M networks.The main objective of this paper is to provide a comprehensive contribution that facilitates the implementation of mechanisms for autonomous scalability management and QoS. This is achieved by integrated the loss recovery protocols, especially those tailored to the Transport Layer of the TCP/IP model, at the middle-ware level within IoT networks.The outcome of this work involves suggesting a new architecture for the existing IoTScal-H approach. It incorporates additional components simulating loss recovery mechanisms to boost the success rate from 95% to 99.99% for e-Health traffic for example, thereby enhancing the current reference approach.
Numerical Study on the Influence of Polyimide Thickness and Curing Temperature on Wafer Bow in Wafer Level Packaging

In wafer level packaging, polyimide and electroplated copper are dielectric and conducting materials respectively in the so-called redistribution layers. During the wafer fabrication process large amount of stress is generated in those layers due to curing shrinkage of the polyimide and the coefficient of thermal expansion mismatch of both materials to silicon which can lead to severe wafer bow after a high temperature curing. In different applications polyimide can be used either only as passivation layer over electroplated copper, or polyimide is applied before the redistribution layer and then a second polyimide layer is applied as passivation. In the current study the effect of different polyimide integrations is investigated. In-situ wafer bow measurements and finite element method studies were conducted with samples, with and without polyimide below redistribution layer, exposed to different curing temperatures to understand the change in wafer bow and copper residual stress. It is observed that wafers with polyimide before redistribution layer show higher stress relaxation in copper with decreasing curing temperature, resulting therefore in a lower wafer bow. A good agreement is achieved between the experimentally measured and the simulated wafer bow.
Virtual Reality in Human Resource Management: Past, Present and Future Trends

This paper presents a comprehensive bibliometric analysis of 416 research articles from 1993 to 2023, specifically focusing on applying Virtual Reality in Human Resource Management. The study delves into publishing trends, prominent journals, and noteworthy contributors, including authors, institutions, and countries. The findings offer valuable insights for researchers, aiding in selecting reputable journals for publication and highlighting key research topics and emerging subfields. Future research should explore integrating VR with emerging technologies like artificial intelligence and data analytics to develop sophisticated HR solutions while also investigating the ethical implications of VR in recruiting and employee training to ensure fairness and transparency. Additionally, exploring how VR can enhance diversity and inclusion in hiring procedures by promoting empathy and unbiased decision-making is a promising area for further study.
Cooperative Transport by Manipulators with Uncertainty-Aware Model-Based Reinforcement Learning

Realization of tasks involving interaction with humans and other robots is important for solving real-world problems. Cooperative transportation is one of the representative tasks. Control methods utilizing model-based reinforcement learning are promising for these tasks under consideration of uncertainty. One of the concerns is, however, the risk of stochastic behavior on the environment during the exploration for learning. Although conventional studies have suppressed undesired behaviors explicitly, the discussions to acquire the suitable model for safe planning are limited. This paper focuses on the fact that model-based reinforcement learning requires long-term prediction, and proposes a method for model learning that takes long-term prediction accuracy into account. Specifically, a new algorithm is developed based on a meta-optimized model learning based on long-term prediction accuracy. The effectiveness of the proposed method was verified through experiments on a cooperative transportation task with two manipulators with force sensors. Experimental results show that the proposed method reduces the worst-case scenario more than the conventional model that merely minimizes the prediction loss of the next step.
A Comprehensive Study on Data Augmentation using Machine Learning with Variational Autoencoders

Data augmentation plays a pivotal role in enhancing the performance of machine learning models, particularly in tasks where labeled data is limited. This paper introduces an innovative approach to data augmentation utilizing Variational Autoencoders (VAEs). VAEs are generative models that learn the underlying structure of the input data, allowing for the generation of new, similar data points. In this paper, we explore the theoretical foundations of VAEs, their application in data augmentation, and present a detailed study on the challenges of this approach in a real-world scenario.
Development of a Retrofit Backhoe Teleoperation System Using Cat Command

Most existing retrofit remote-control systems for backhoes are large, hard-to-install, and expensive. Therefore, we propose an easy-to-install and inexpensive teleoperation system. The proposed system comprised remote-control and sensing systems. The remote-control system retrofits robot arm-based devices to “Cat Command”, a compact embedded teleoperation system with a limited communication range, and controls these devices via a 5G commercial network to realize control from a remote office. Because this system does not require any additional modifications to the embedded control unit in the cockpit, the operator can continue working in the cockpit even if the backhoe is remotely controlled. The system enables the remote control of various devices from an extremely long distance by changing the joint parts between the robot arm and the embedded remote-control device. The sensing system estimates the posture and position of the backhoe by attaching original sensing devices to the backhoe. In addition, a 360 camera was installed in the cockpit to transmit work images from the construction site to a remote office in real time. The sensing device was smaller and lighter than conventional devices. We confirmed that the proposed system can be used to operate a construction site backhoe from a remote office, and that the system can be used to excavate soil using an actual backhoe.
Malware Detection in Industrial IoT using Federated Cloud

Using BaIoT, a network stress simulator for actual IoT devices that have been compromised by computer viruses, the suggested methodology was developed. The ability to identify malware that impacts both visible and hidden N- BaIoT IoT bias has been taught and validated using supervised and unsupervised allied models. Furthermore, their performance has been evaluated using two standards. In fact, it appears that the step used in the majority of related literacy algorithms, the birth model aggregation composed phase, is particularly vulnerable to multiple attacks made against a single adversary. The effectiveness of colourful model aggregation procedures as a deterrent is therefore evaluated using attack scripts. This feature significantly improves the defences against malicious actors, while developing allied tactics will require more work.
Novel Ultrawide band Multi Input/Multi Output Antenna Design with Single Notch Characteristics and Investigation with Improved Isolation

The research work proposes a MIMO i.e. multi input multi output antenna in circular shape with sufficient isolation for ultrawide band applications with single notch characteristics. To reduce mutual coupling among MIMO elements, a decoupling stub is utilized, which can reach up to - 50 dB over the bandwidth. The suggested antenna is suitable for usage in both UWB applications. The suggested antenna is 28*26*1.57 mm3 in overall dimension. In the range of 3–12 GHz, the S11 i.e. the return loss parameter and VSWR are less than -10 dB and 2, subsequently for the considered ultrawide band MIMO antenna. The recommended ultrawide band antenna design has an impedance bandwidth of about 9 GHz. The recommended antenna is designed and simulated in HFSS.
Application of Particle Swarm Algorithm in Multi-objective Shop Floor Scheduling for Intelligent Manufacturing Systems

In the intelligent manufacturing system, the assembly line is an important process in the production process. Its main purpose is to process the raw materials according to a certain process to make products that meet the needs of users. In the production process, the assembly line workers can realize the workpiece processing through the operating table, and can convert it into machine energy. As a complex and closed entity, the workshop needs to deal with many links. Therefore, for the whole production line scheduling problem is also very critical and has research value; Based on particle swarm optimization algorithm, this paper organizes and manages a workpiece reasonably and effectively on the production line to realize the automation of shop scheduling, and finds that it has a good effect in practical application, which can bring considerable economic benefits to the enterprise. Therefore, it is of great significance to use the method of multi-objective shop order task scheduling to improve productivity and reduce workload.
Learning and Generalizing Tasks on Humanoid Robots with an Automatic Multisensory Segmentation Method

We provide a complete framework for learning and reproducing tasks from human demonstrations. This framework adapts recent developments in automatic, unsupervised segmentation of time-series to humanoid robotics by preprocessing the data obtained from a broad range of the robot's sensors, to then repropduce the learned task in similar environments. In more detail, we reproduce and extend the acquired multi-step task using Dynamic Movement Primitives in simulation for the JVRC1 Robot, and further validate it the segmentation process in real world with the HRP-4C Robot, thus showcasing the possibility to create an extensive library of reusable skills for complex humanoids with our approach.
Diabetic Foot Ulcer Detection on Mobile Platforms through Thermal Imaging and Deep Learning

Diabetes mellitus, causing substantial global mortality with millions affected in 2019, necessitates early detection methods to aid Diabetic Foot Ulcer (DFU) patients. We proposed a mobile application for early DFU identification using a Convolutional Neural Network (CNN) classifier. The application embedded a fine-tuned MobileNetV2 model specifically adapted for DFUs, trained on a plantar thermogram dataset through transfer learning. We evaluate the model’s classification performance pre- and post-embedded into the application. Results reveal that the pre-embedded model achieved 90% accuracy, 100% specificity, and 80% sensitivity. After embedding, the model retained 85% accuracy, 80% specificity, and 90% sensitivity. Notably, a slight accuracy decrease post-embedded is likely attributed to the normalization of the input image. This study emphasizes that the interplay between model performance and mobile embedded might influence the model’s performance. It underscores the meticulous consideration of mobile platform-specific processes crucial for accurate and effective early DFU detection.
Leveraging Deep Belief Networks and Multi-Class Support Vector Machine for Nutritional Deficiency Identification in Grape Plant Leaves

Modern fertilization includes identifying nutritional deficits in plant leaves since soils frequently fall short of providing growing crops with all the nutrients the earth needs. The creation of a fertilization program to deliver the desired micronutrients while not overproducing others will be made easier by determining the amount of vitamins and minerals that are required. The indications of agricultural malnutrition are nutritional insufficiency indicators in plant leaves, which may be physically examined from the plant morphological elements and so offer adequate information to identify the nutritional deficit. This study was intended to focus on nutritional deficiency identification in grape plant leaves. In this work, Deep Belief networks (DBNs) are employed for training the model. The output of the model is fed to a Multi-Class Support Vector Machine (SVM) to classify the grape plant leaf images as healthy, N-deficiency, P-Deficiency, K-Deficiency, Ca-Deficiency, Mg-Deficiency and S-Deficiency. Accuracy and Misclassification rate is used to analyze the performance of the model. The results indicated that the suggested model achieved an accuracy of 96.8% and a misclassification rate of 3.2%. These results show that DBNs are a potential technology for nutritional deficiency categorization in grape plant leaf tissue.
Real-Time Marker-Based Monocular Autonomous Docking in Semi-Unstructured Indoor Environments*

Environmental changes can severely disrupt the docking sections of transportation systems in factories. Such disruptions not only halt the production line but also necessitate human intervention for loading and unloading, posing safety risks. To address this, we introduce an autonomous docking system designed for resilience in semi-unstructured environments, especially when faced with varying light conditions and physical alterations. Our solution involves an advanced ArUco marker system. By integrating the yaw orientation of the marker with angular velocity data from an inertial measurement unit, we achieve a homogeneous matrix reconstruction. This enhanced marker data then guides a reference trajectory for docking, controlled by a PI Pure Pursuit mechanism. To further improve marker recognition, we have incorporated reflective and anti-reflective materials and modulated the marker's white margin. Our test results indicate a significant improvement in the detection range of a 21cm marker expanded from 60cm to 2m. Moreover, our system ensures a docking precision of $\pm 31\text{mm}$ and $\pm 1.82^{\circ}$.
An Efficient Noise Reduction Technique and Classification of Osteosarcoma by the Machine Learning Approach

Cancer is the one of the most dangerous and uncontrollable diseases in this world. It spreads rapidly in living organisms. To identify the disease early from various parts of the body medical image processing plays a major role in the medical field. Among the various diseases; an osteosarcoma is the most dangerous disease and leads to death in human life. To classify the sarcoma with early symptoms and accurate analysis with less noise in prediction has a major role in the medical field. To reduce such complexity, machine learning techniques has used. It contains large number of data set with various morphological and operational techniques. To classify the images primary methods such as preprocessing, edge detection are used. In continue with primary methods segmentation along with feature extraction are used to classification of images with the help of support vector machine. It has a special feature to classify all the images exactly based on input data. With the help of training and testing data in SVM classifier classify the cancerous and non-cancerous image with efficient accuracy with reduced time.
An Advanced Feature Selection Approach to Improve Intrusion Detection System using Machine Learning

This research study presents an efficient threat detection model in which primary emphasis has been placed on the feature selection and classification phase The primary objective of the proposed model is to enhance the accuracy of intrusion detection while decreasing its complexity and false alarm rates. In this model, entropy-based infinite feature selection, Eigenvector centrality, and ranking feature selection algorithms are used for selecting only significant and critical features. Moreover, three classifiers, i.e., Artificial Neural Network (ANN), K-nearest neighbor (KNN), and Decision Tree (DT), are used as classifiers for detecting and categorizing potential attacks that may take place in the IoT network. KDD-CUP99 and NSL-KDD were used as benchmark databases to test the performance of the suggested technique in MATLAB. The results revealed that the proposed DT and KNN are generating the most promising results compared to the rest of the models in terms of accuracy, precision, recall, and F1 score.
Goal-Oriented Task Planning for Composable Robot Control System Using Module-Based Control-as-Inference Framework

Robot control systems built with flexibly combinable sub-task modules can be applied to diverse and complex tasks by reusing or recombining those modules. Previous studies have proposed multiple module designs to achieve a goal task, but they require learning or manual design of dependencies between modules, and in a number of cases, learning the order of execution of modules toward a goal, which is a challenge for a composable robot control system in diverse applications. To solve this problem, we propose goal-oriented task planning based on inter-module similarity. As transitions between modules are possible when the termination of one module is similar to the initiation of another, our method defines the state distributions at the initiation and termination of each module, and automatically estimates the dependencies between modules on the basis of their similarity. We further developed a goal-oriented task planning method by incorporating these modules into the framework of Control as Inference [1], [2]. We evaluated our method in an environment simulating a kitchen task using an arm robot and showed that it performs as well as or better than conventional methods without manual design and learning of dependencies between modules.
Integration of Origami Twisted Tower to Soft Mechanism Through Rapid Fabrication Process

Origami structures have been widely integrated in various applications in soft robotics, especially origami twisted tower with continuum mechanisms. However, it is difficult and time-consuming to integrate them into existing structures, since the fabrication processes were conducted either manual folding or 3D printing. To address this issue, we propose a rapid fabrication approach for the origami twisted tower as soft mechanisms with design interface and laser cutting. Following the crease patterns of the fabricated diagram, we verified the origami material and layers with evaluation experiments and found polypropylene as the most suitable material and stiffness for potential applications of the origami structures.
ALEXNet Optimized Cross layer Convolution Neural Network Algorithm for Efficient Diabetic Foot Ulcer Identification

Increasing variety of disease in daily fact affects the human nature, as far diabetic is most harmonic disease creates illness to affect the health. In such a way Diabetic Foot Ulcer (DFU) cause complication which is not controlled to leads diabetic treatment failures. By identifying the disease accurately is the most challenging factor because of prevailing techniques doesn’t concentrates the peripheral segmentation through image processing. This leads inaccurate to detect the disease effectively to find the risk factor. Still, the heterogeneity of diabetic skin lesions and the presence of noise in images taken with digital cameras make lesion extraction a difficult task. To resolve this problem, we presents a new method of Cross Layer Convolution Neural Network (CLCNN) is employed to improve the performance accuracy to contribute feature evaluation perfectly in segmentation level to find the ulcer. Initially preprocessing was carried out by Weiner Filter (WF) and Z-Score Monochrome Normalization (ZSMN) is applied to remove the noise to enhance the image quality. Then Color Quantum Histogram Evaluation (CQHE) is applied to find the spectral margins of wound region to increase the object entity parts findings, ALEXNet optimized Cross layer convolution neural network with ReLU activation function to increase the identification accuracy. Therefore, the experimental result provides high accuracy, sensitivity, specificity, ROC and F-score performance.
Assembly of Printed Interconnects for Immobilized Protein Microfluidic Assays

Microfluidics has attracted significant attention for biological applications, particularly for high-throughput screening and multiplexing. However, conventional interconnect techniques are limited in their ability to fabricate the hundreds or thousands of microfluidic interconnects needed to process multiple samples for high-throughput microfluidics. To address this challenge, coaxial printing was invented to enable rapid, customizable, and scalable microfluidic interconnects. This paper focuses on the assembly process of coaxial printed interconnects for immobilized protein microfluidic assays. We demonstrate the immobilization of protein into a 1 × 1 microwell microfluidic unit with printed connectors to showcase the process.
Design of Microstrip Patch Antenna [MPA] for ISM Band of Applications

This research study showcases the detailed design of (MPA) for International Scientific and Medicine band (ISM). The selected substrate material for the antenna is FR-4, which offers suitable properties for its performance. The antenna operates at a frequency, specifically within the ISM band. The development of this antenna prioritizes several advantageous features such as low cost, simple construction, minimum profile with maximum isolation. These characteristics contribute to the antenna overall efficiency and usability. The design of the antenna will involve the utilization of CST simulation software in order to achieve a desirable return loss. The use of CST simulation software will allow for effective analysis and optimization of the antenna's performance.