Miracle-dz/UniFAR-FTUs huggingface dataset:
Convolutional Neural Network for Printed circuit Board Verification

One of the most important steps in the manufacturing of electronic goods is the creation of printed circuit boards (PCB). An electronic device's PCBs are the first stage in production, so even a small error there could cause serious flaws in the finished product. Finding and identifying flaws in PCBs is crucial given the size and demand of the PCB industry. In this paper, we propose a reference comparison method for PCB defect localization and classification that employs a template and a test picture and integrates machine learning and image processing techniques. We introduce a solution pipeline that works in three stages: Subtraction of Images where the test images are subtracted to generate a mask that highlights the regions of defect on the test image, Contour search to find the contour of the defects from the generated test image, and Model Inference where the contours found above are extracted from the images and fed into our machine learning model which then classifies those defects in the test image. We train the machine learning model, in our case, a Convolutional Neural Network (CNN) that forms the artificially intelligent (AI) part of our solution which can identify the type of defect present given the image of the defect as input. Our experimental findings demonstrate that, compared to other traditional methods utilized for this work, our model diagnoses the afflicted parts with 97% accuracy.
Behavioral Intention to Invest Cryptocurrency in Indonesia: An Empirical Study

The use of blockchain technology is increasing along with the need for an increase in financial value, one of which is cryptocurrency. This study analyzes behavioral intentions to invest using cryptocurrencies based on social effects, regulatory support, and trust factors. Quantitative procedures are applied to answer study hypotheses with data sources from distributing online questionnaires to respondents who invest in cryptocurrencies in Indonesia. Therefore, a purposive sampling technique was applied to 410 respondents. The data analysis used Partial Least Square - Structural Equation Modeling-(PLS-SEM). The study's results prove that social effects positively and significantly influence trust and behavioral intention. In addition, regulatory support positively and significantly affects trust, and trust has a positive and significant effect on behavioral intention to invest in cryptocurrency. This research contributes significantly to investor behavior by applying regulatory support for adopting the latest information technology.
Deep Reinforcement Learning-Based Control of Bicycle Robots on Rough Terrain

The bicycle robot is a kind of unmanned mobile robot with great potentials. However, the control of such robots on rough terrain under model uncertainties and disturbances is challenging due to the lateral instability and underactuated char-acterisitcs. In this paper, a controller based on deep reinforcement learning and Stanley algorithm is designed to achieve the path tracking and balancing control for a bicycle robot on rough terrain. First, the outputs of the controller are compensated by residual reinforcement learning to reduce the gap between the training environment and the test environment. Then, in balancing control, the piecewise curriculum learning is designed to accelerate the training process. In path tracking control, Stanley algorithm is used. The proposed algorithm is tested in Gazebo simulation environment. The results showed that the proposed algorithm achieved good performance.
Bogs: Surface Patterns And PatternClassification

Pattern recognition and classification has so far remained a largely unexplored area of peat-land science. The objective of this work is to classify the surface patterns displayed on an aerial photo of Kentra Moss, a bog (mire) in Western Scotland, UK. The computer experiments reported in this research present the attributes of image textures calculated using statistical texture measures, e.g., mean, variance, skewness, kurtosis, and entropy. Insignificant attributes are eliminated by using statistical techniques. On the other hand the clusters are obtained by calculating the Euclidean distances among all exemplars. The importance of this work is the provision of an objective method for classifying mire surface patterns, much needed but so far not available.
Empirical Study of Different Machine Learning Techniques in Classifying Aspergillus Species

Aspergillus, a widely-known genus of Conidialsaprophytic fungi, consists a number of species under the taxonomic group which possesses a significant role in both public health and the food industry. Aside from microscopic observation and molecular detection of Aspergillus species, it has become important to develop a computational approach to classify the species from their DNA sequences more efficiently. Studies prove that machine learning algorithms which can learn from observed data directly have emerged as the most dominant practice in jobs like classification, prediction etc. This research highlights on the comparison of different machine learning classifiers based on DNA K-mers.
Financial Performance of Aviation Firms

Financial performance measurement has a vital importance for companies since it demonstrates the companies' success in achieving their targeted goals. In this study, financial performance analysis was performed using the data of four large airlines with large market shares operating in the United States. In the study, multi-criteria decision-making methods were used. Multi-criteria decision-making methods are generally applied in two steps, the first is to determine the criteria weights, and the second is to determine the company's performance. The CRITIC method was used to determine the weights and the SAW method was used to evaluate the alternatives. According to the results of the CRITIC, the criterion with the highest weight is determined as revenue per seat mile. According to the SAW results, Southwest Airlines is the best performer. When the analysis results and financial data of the companies were examined, it was concluded that Southwest's business model, market and healthy load factors enable the airlines to be successful.
A Review of Automatic Road Sign Recognition Systems

Traffic-signs recognition(TSR) systems are an in-herent element of Intelligent Transport Systems (ITS), which is why it's considered a necessary element of Advanced Driver Assistance Systems (ADAS). One of our biggest challenges for driver assistance systems requires realizing the surrounding environment while driving the car. This paper details standard Traffic-signs recognition(TSR) system methods used in recent research. Features and classifiers used in modern TSR system methods are introduced and discussed. Most of these methods achieve great precision (accuracy of 98%) when tested in the German Traffic Sign Recognition Benchmark (GTSRB), the most extensively employed and publicly accessible TSR dataset. The paper compares the latest techniques by evaluating their processing time and precision. Furthermore, a discussion of possible future work is provided.
Tracking Fractional Power Rate sliding mode control for a 4DOF manipulator robot

This study presents a control strategy combining fractional calculus and the power rate sliding control theory for a manipulator robot with Four Degrees of Freedom (4-DOF). The suggested Fractional Power Rate Sliding Mode Control (FPRSMC) aims to track both joint space and workspace desired trajectories. The enhancements to the sliding mode control include applying fractional calculus to the control law’s sliding surface to improve the power law reaching mode. Experimental evaluation of the suggested control method is performed on a MICO 4-DOF manipulator robot, and it is contrasted with an Improved Power Rate Sliding Mode Control (IPRSMC) optimized with Particle Swarm optimization (PSO) to demonstrate its efficiency and performance. The closed-loop system’s stability is also proven by applying the Lyapunov theory. The findings show that the suggested control achieves better tracking performance than the optimized improved power rate sliding controller.
Neural-adaptive Quantized Consensus Tracking Control of High-order Power-chained Nonlinear Multi-agent Networks with Switched Dynamics: A Specified-Time Convergence Protocol

This study investigates the neural adaptive quan-tized consensus tracking control of high-order power-chained (the system powers are odd positive integers rather than one) nonlinear multi-agent networks with switched dynamics using a specified-time convergence protocol. Based on the newly designed specified-time performance function (STPF), a novel tan-type high-power asymmetric barrier Lyapunov function is devised, such that the consensus tracking error of each follower agent constrained in the pre-assigned performance bounds within a specified-time. The input quantization is considered to release the communication burdens, by utilizing a variable-separable lemma which extracts the quantized signals in a “linear-like” manner ingeniously. The neural adaptive controller is constructed by combining adding-one-power-integrator scheme, RBFNNs based approximators, and proposed STPF method. It is strictly proved that the local consensus tracking error of each agent eventually converges to a small compact set, while all the closed-loop signals remain bounded. In addition, comparative simulations are performed to validate the superiorities of the obtained theoretical findings.
The Shape of the Homunculus Nebula around η Carinae

Abstract From extensive spectroscopic observations we derive the three-dimensional structure of the Homunculus Nebula surrounding η Carinae. The analysis shows that the two lobes are hollow and incomplete, and that the gas was ejected at similar epoch. Consequently the distribution of material we see reflects its initial velocity. Using the observed expansion velocity, and the known time of ejection, we derive a distance to η Car of 2.2 ± 0.2 kpc.
KeLLy – Efficient, Scalable Link Layer Topology Discovery

Network infrastructures are becoming increasingly flexible and dynamic not only due to softwarization and virtualization, but also due to increasing mobility in 5G and 6G networks, which consider drones and satellites to be part of the core infrastructure. Since the network topology may change frequently, it becomes challenging to get an up-to-date view of its current state. This paper introduces KeLLy, an efficient, scalable link layer topology discovery algorithm focussing on large-scale networks (evaluated up to 100,000 nodes). KeLLy discovers various large topologies in seconds, guarantees discovery of all nodes (and a high percentage of links), while inducing low, predictable overhead by querying only a subset (4%) of nodes.
Machine Learning Based Intrusion Detection System for Network Security Using Self-Organizing Map

Machine learning techniques are widely used for detecting network attacks at the network level and the host level in a timely and automatic, to develop an intrusion detection system (IDS) for grading. Malicious attacks are always the need for change and scaling solutions, because it occurred during a very large volume, however, a number of challenges will occur. There is a data set that can be publicly available for further research on another malware of cyber security within the community. Network, plays an important role in modern life, has become the network security is an important field of research. Is an important network security technology Intrusion Detection System (IDS) being to monitor the software running on the network and hardware status. Despite the decades of development, the existing IDS, still, to reduce the improved error rate detection accuracy, are faced with the challenge of detecting unknown attacks. To overcome the issues proposed the method is Self-Organizing Map (SOM) Performance of intrusion detection depends mainly on the accuracy. Accuracy intrusion detection is to reduce the error rate, it must be strengthened in order to increase the detection rate. In order to improve performance, different technologies, have been used in recent works. It is the main work of the intrusion detection system for analyzing a huge network traffic data. Well-organized classification method, you need to solve this problem.
Responding to Network Failures at Data-plane Speeds with Network Programmability

Measurement studies show that equipment failures happen quite frequently and pose a challenge to reliable network operation. Quickly recovering from failures is critical to meeting service guarantees. Traditional routing protocols, due to being executed in a distributed fashion and involving multiple devices in a network, require non-negligible time to recompute routes upon failures. SDN with OpenFlow simplifies route recomputation, but the time to compute and install alternative forwarding entries can still result in significant packet loss. Existing fast failover mechanisms cannot handle all types of failure and do not guarantee the use of the best paths. In this paper, we present FELIX, an approach for failure recovery that reroutes around failures at data plane timescales. Felix works by efficiently pre-computing tactics to handle failure scenarios that can be quickly activated in the data plane in response to failures. Our evaluation shows that our approach can recover from failures up to three orders of magnitude faster than existing SDN approaches.
Development of Cloud Centric Healthcare Monitoring System For Cardiac Patients embedded in IoT Platform

In terms of profit and employment, the Indian economy’s healthcare sector has emerged as one of the most significant sectors. The Indian healthcare sector is experiencing rapid growth due to improved coverage, expanded functionalities and services and increased public and private spending. Due to which the life expectancy has increased dramatically. As the population of individuals rises in many societies, the number of people affected by chronic diseases, such as diabetes, cardiovascular disease, obesity, and so on, rises as well. As a result, Internet of Things (IoT)-based personalised healthcare is becoming a more significant idea. This paper’s main goal is to illustrate a cardiac patient e-health data acquisition, transfer, and monitoring system. The smart health monitoring system prototype uses a wireless sensor network to measure various health data, which are then sent to the concerned individual through GPRS. These parameters are also saved in Amazon cloud services for future use.
Radar-Based Multiple Target Classification in Complex Environments Using 1D-CNN Models

In this paper, we propose a robust multiple target classification algorithm for real-world complex cluttered environments that can be mapped into low-cost millimeter-wave (mmWave) sensors considering limited memory and processing power budget. A novel approach is developed to create both μ-Doppler and μ-range spectrogram of multiple objects concurrently using an extended Kalman filter (EKF) based tracking layer integration. One-dimensional (1D) time sequence features are extracted from both spectrograms per target object, and a 1D convolutional neural network (CNN) based classifier is built to classify multiple target objects (human or non-human) in the same scene accurately.
Design, Development and Implementation of an Effective and Efficient Emergency Third Rail Fishplate Joint Clamp

A third rail joint is an indispensable asset ensuring electrical continuity and third rail alignment in a railway network. When a faulty third rail joint issue is reported during train revenue service, track maintenance staff will implement an interim solution to address it. However, considerable time and effort are often spent on the emergency recovery process. This paper, therefore, aims to design, develop, and implement an Emergency Third Rail Fishplate Joint Clamp (ETJC) that can effectively and efficiently overcome faulty third rail joint issues, while minimizing operational costs. ETJC design, modeling, and simulation were performed using Creo Parametric and Ansys software packages. A static structural analysis of ETJC was conducted. Materials for manufacturing ETJC were carefully selected to withstand any loading and environmental conditions that may occur on the ground. The ETJC was implemented in a train depot track and evaluated via hammer impact tests. Acceleration signals were acquired and analyzed at different locations on the third rail joint with ETJC. Results from simulation and field experiments are consistent and promising, thereby implying that the ETJC can firmly support and align a defective third rail joint.
Hybrid-Vehicle Purchase Intention in Indonesia: Does Environmental Concerns or Knowledge Matters?

Hybrid vehicle is one of the solutions to reduce the threats of environmental degradation. This study aims to analyze the effect of environmental concerns and hybrid vehicle's knowledge on purchase intention of hybrid vehicle in Indonesia. A sample of 211 car owners in Indonesia have been collected to predict the intention to purchase hybrid vehicle using the theory of Planned Behavior. The empirical results show that the environmental concern and hybrid vehicle knowledges have direct effect on purchase intention mediated by attitude, subjective norms, and perceived behavior control. The implication for purchase intention of hybrid vehicle is discussed.
A DoS Attack-Resilient Grid Frequency Regulation Scheme via Adaptive V2G Capacity-Based Integral Sliding Mode Control

The latest developments in vehicle-to-grid (V2G) technology enable electric vehicles (EVs) to participate in frequency regulation (FR), reducing the impact of uncertain power fluctuations in highly penetrated renewable resources. However, the performance of FR is threatened by cyber attacks that compromise control communications. Considering denial of service (DoS) attacks on EV communications, this paper proposes a resilient FR scheme via adaptive V2G capacity-based integral sliding mode control (ISMC). The proposed scheme utilizes ISMC to mitigate uncertain disturbances from renewable resources and loads and the impact of DoS attacks. A periodic event-triggered mechanism (PETM) is integrated into the ISMC to reduce communication burden. Importantly, an adaptive mechanism of V2G frequency regulation capacity (FRC) is proposed in ISMC to adapt to the estimated attack intensity to prevent DoS attacks from further deteriorating FR performance. The proposed adaptive mechanism recommends using a conservative V2G FRC profile to improve FR performance under high-intensity attacks. Two case studies of the IEEE-39 bus system illustrate the effectiveness and advantages of the proposed scheme.
BQBCC: Design of an augmented Bioinspired model for improving QoS of Blockchain IoT deployments via Context-based Consensus

Blockchain-deployments are highly secure, but lack in terms of scalability due to exponential increase in mining delay w.r. t. chain lengths. To overcome these issues, researchers have proposes used for low-complexity mining, sharding techniques, and other machine learning optimizations. But these models either depend on underlying blockchain, or showcase larger computational delays, which limits their scalability levels. Moreover, most of these models do not consider consensus optimizations, which further limits their deployment capabilities for large-scale networks. To overcome these issues, this text proposes design of an efficient bioinspired model for improving QoS of blockchain IoT (Internet of Things) deployments via context-based consensus. The proposed model initially collects temporal mining performance from existing miner nodes, and deploys a novel Proof-of-Temporal Trust (PoTT) based consensus for validating responses of these miners. The PoTT Model uses temporal mining delay, energy consumed while mining, and throughput levels for selection of high-performance miners for processing block-addition requests. Requests approved by these miners are stored on a set of Bacterial Foraging optimized (BFO) sidechains. These sidechains are automatically tuned based on spatial QoS performance of the network under real-time conditions. The BFO Model assists in segregating existing single-length blockchains into QoS-optimized sidechains. To perform this segregation, the BFO Model uses an exhaustive consistency metric that combines QoS & security levels that can be applied to specialized applications like Industrial IoTs. Thus, segregation into sidechains is done while maintaining high security under heterogenous attacks. Due to these optimizations, the model was able to reduce mining delay by 3.9%, reduce energy needed for mining by 2.5%, improve throughput by 4.5%, while maintaining high attack-detection efficiency under Sybil, Distributed Denial of Service (DDoS), and Masquerading attacks.
Reinforcement Learning based Cloud and Edge Resource Allocation for Real-Time Telemedicine

Future healthcare services will extensively exploit wireless telehealth solutions in various healthcare use cases from preventive home monitoring to highly demanding real-time scenarios, such as monitoring an emergency patient's vital functions in an ambulance or ICU unit. Reliable real-time communications and computing are needed to enable these highly critical health services. However, the majority of current telehealth use cases are cloud - based, which poses a challenge to provide sufficient Quality of Service (QoS). The traditional centralized cloud infrastructure cannot meet the latency and reliability requirements due to long and unreliable communication routes. Therefore, the most advanced cloud solutions integrate edge computing as an integral part of the computational architecture to bring a part of the computational infrastructure to the proximity of the data sources and end-nodes, thus constituting an edge-cloud continuum. This continuum is capable of serving applications with real-time requirements. However, since edge computing capacity is a limited resource, solutions are needed for deciding which tasks should be run on edge and which at the data center. In this paper, we propose a machine learning-based solution to prioritize ultra-low-latency tasks for running on the edge to meet their strict delay requirements while leaving other tasks to be executed at remote servers. Our proposed solution in comparison to the baseline has a significantly lower dropping rate and outperforms fixed - interval scheduling solutions in terms of resource efficiency.