Miracle-dz/UniFAR-FTUs huggingface dataset:
Lightweight Design and Finite Element Analysis of Brake Lever for Motorcycle Application

A lightweight component design contributes to the overall optimization of a system to be more effective and efficient. Then, it can lead to the contribution of a carbon footprint reduction. The research aimed to propose a novel lightweight brake lever design for motorcycle applications and numerically investigate its performance by comparing the proposed design with different utilized materials. The subject of the research was an optimized brake lever for motorcycle application. The materials used were aluminum alloy, structural steel, and titanium alloy. A Finite Element Method (FEM) analysis was employed to investigate the proposed brake lever design. Three proposed designs were introduced with the mass reduction in each optimization up to 50,9% of reduced mass. Maximum stress was observed on the most optimized design with a value of 297 MPa. The strain and total deformation were also investigated among the components. In the result, the stress-strain graph shows that the most optimized brake lever experiences the highest stress with the highest strain value. Furthermore, the highest safety factor is achieved with the utilization of titanium alloy, reaching the value of 6,28 for preliminary design and 3,1 for the most optimized component. However, the lightest component can be obtained using aluminum alloy.
Underwater Acoustic Location Estimation of Flight Recorder Using Onboard Ad Hoc Wireless Sensor Network

Underwater Wireless Sensor Networks (UWSN) are an upcoming technology for various underwater applications, such as environment monitoring and localization of objects. An important use case for underwater object localization is finding a flight recorder (black box) after an oceanic flight crash. Current technology for finding this black box uses hydrophone-equipped vessels or autonomous underwater vehicles (AUV). However, these methods arrive too late at the crash site complicating the search process. In this article, an alternative approach to locating the sinking black box is presented utilizing an UWSN onboard the aircraft. By registering acoustic ping signals of the underwater locator beacon (ULB) of the flight recorder, the nodes in the network collaboratively track the location of the sinking black box using a Time-Difference of Arrival (TDoA) approach. Underwater nodes are used to extend network coverage to deep-ocean levels and communicate their findings by utilizing wireless acoustic modems. Simulation results show that this approach enables locating the black box with an error less than 50 meters within 25 minutes after the first ping was sent by the ULB, outperforming current vessel-or AUV-based localization approaches in time and coverage.
Implementing a D2D e Network Using Fuzzy C Means Clustering for Emergency Situations

D2D network is very promising for emergency situations. As D2D depends upon the proximity between the users which reduces the battery consumption for short distance communication and also with the help of fuzzy C means clustering algorithm a network for communication can be established. In this arrangement group of devices are considered as clusters and cluster centroid is found for cluster at which relay device is placed. And we analyze this network and compare the parameters like energy consumption, spectrum and interferences of cellular network and this clustered D2D network. So, by comparing both networks schemes we can conclude that which network is good for better means of communication in case of emergency situations.
Design of Compact Circular Shaped Slot Antenna for Wireless Sensor Networks

This study is to improve the implementation and design of microstrip patch antennae for real-time wireless Sensor Network (WSN) applications. By forming a multi-hop self-controlling network with different topologies by connecting end nodes with radio frequency communication. The designed circular-shaped slotted patch antenna operates in the ISM band of 2.4 GHz, FR-4 substrate for antenna has been designed using HFSS software. The proposed antenna is widely used in short-range wireless sensor networks. A low return loss, wideband, and compact design circular shape slot antenna enhancing the wireless Sensor communication applications at the resonant frequency.
Study on the Electrical Tree Propagation Evolution in Polymer Composites via Phase Field Method

Polymer composites have been broadly used in electrical and electronic applications, in which fillers are incorporated into polymers to achieve better properties. As electrical trees are a typical cause of insulation failure in dielectric polymers, the addition of fillers in polymer composites has also raised new problems concerning electrical treeing. The phase field method was utilized in this work to simulate the propagation of electrical trees in polymer composites. The permittivity of fillers was proved to influence the trajectory of treeing, which should be attributed to the distorted electric field around the fillers. The propagation rate in terms of tree length and damaged volume with respect to time was calculated, which was affected by the critical energy densities of the fillers and polymers.
Simulation of the 5G Communication Link Between Solar Micro-Inverters and SCADA System

Integration of Distributed Generation (DG) into the existing grid, and communication being the lifeblood of any such system, is the answer to the rising demand for power. The characteristics of Wi-Fi are shared by 5G connections, which offers a peak speed of 1 gigabyte per second and extremely low latency (1ms), unlike 4G. To ensure an uninterrupted flow of power, this research focuses on investigating and establishing 5G communication protocols between the SCADA system and the solar micro-inverter of the solar power system. The 5G architecture protocol is designed on the NetSim simulator, which is utilized to gather and evaluate data, while the power system simulation is carried out in MATLAB Simulink. The simulation results show that 5G communication was successfully implemented between Solar Micro-Inverters and SCADA systems.
Predicting the Life of the Solder Joints in Electronic Assemblies using Physics-informed Data-driven Methodology

The accurate prediction of the life of solder joints is critical to the design of electronic devices. However, accelerated life tests used to estimate the reliability of solder joints are costly and time-consuming due to the multiple implementations of tests under different conditions. Furthermore, it is impractical to incorporate various factors affecting life into a single physics- based model. This paper proposes a physics-informed data-driven methodology to evaluate the impacts of different factors on the life of solder joints and improve prediction accuracy. Various factors (such as aging conditions, component configuration, board substrate material, solder paste material, solder sphere alloy, and stencil thickness) and their effects on life have been explored. Machine learning methods, including linear regression, decision trees, and artificial neural network (ANN), are used for life prediction because of their distinct advantages in accuracy and interpretability. For instance, the ANN method shows the highest prediction accuracy (R2 = 92%). This physics-informed data- driven methodology will be a starting point for future efforts in leveraging physics and experimental data in electronics reliability analysis.
Investigating 5G Network Slicing Resilience through Survivability Modeling

The deployment of 5G networks has brought significant advancements in mobile communication, enabling novel applications and services with diverse requirements. Network slicing, a crucial technology for realizing 5G’s full potential, allows operators to create multiple, logically separate networks on a shared physical infrastructure. However, ensuring the resilience and survivability of network slices during network failures is a crucial challenge that needs to be addressed, particularly for critical infrastructure services. This paper presents an ongoing research project investigating the survivability of network-sliced 5G systems during network failures, focusing on the ClusPR framework for service chaining and resource allocation.The study aims to: 1) develop a framework for assessing the survivability of network-sliced 5G systems during and after failures; 2) explore factors influencing the resilience of various slicing approaches in challenging events; and 3) apply findings to design resilient 5G networks and develop strategies for handling undesired events. As the project advances, the expected results will provide valuable insights and heuristics into proactive and reactive strategies for maintaining the continuous operation of critical services in 5G networks during network outages.
Design of Scanning Fiber Micro-Cantilever based Catheter for Ultra-Small Endoscopes

The continuous research into optics and micro-electro-mechanical systems (MEMSs) enables the fabrication of optical scanners having diameters as small as few millimeters. As a result, medical practitioners can image smaller areas of the body that were inaccessible in the past. The overarching goal of this work has been to develop a sub-millimeter sized optical scanner that provides a foundation for a miniaturized endoscope able to image previously inaccessible luminal organs in real time. The proposed scanner consisted of a vibrating optical fiber that carried laser light to the target imaging area, with a peripheric ring of more optical fibers to capture the backscattered reflected light within the same device. The desired vibratory motion of the scanning cantilevered fiber was obtained using an electro-thermal actuator which periodically excited the distal end of the scanning fiber to provide movement.
Research on Point Cloud Registration Network Based on Dynamic Graph Convolution

The three-dimensional images collected by laser sensors from a single perspective are incomplete, and obtaining a complete point cloud requires registering images from various perspectives. In this paper, we propose SL-DGCNN (Strength Local Dynamic CNN) which enhances the extraction of local features of key points normal vectors and point pairs by designing a weighting function, and optimizes the network using a new aggregation function. We verify the effectiveness of the proposed method using the DCP registration network, and experimental results show that the registration model based on our improved method has significantly improved registration effects in metrics such as rot_rmse and trans_rmse.
Gradient-Guided Multi-Modal Image Reconstruction for Electrical Impedance Tomography

The low image quality of Electrical Impedance Tomography (EIT) hinders it from performing quantitative analysis in various fields like tissue engineering. This paper presents a gradient-guided multi-modal image reconstruction algorithm (named GGIR) for high-quality EIT image reconstruction. In the GGIR, a carefully designed first-order gradient-based regularization term serves as the interface incorporating structural information from the auxiliary image. The Laplacian regularization is also included in the GGIR for image smoothing. The performance of the proposed GGIR is evaluated by numerical simulation and real-world experiments. Compared with given single-modal and multi-modal algorithms, the proposed GGIR is superior in image quality improvement with a 40.2% ∼ 90.3% reduction in Err and a 16.5% ∼ 180.2% increase in MSSIM in the numerical simulation. Visual results in the simulation study and real experiments also prove the superiorty of the proposed GGIR. These evidences indicate that the GGIR has the potential to be applied to complex tissue engineering applications and regenerative medicine.
A Hybrid Method for Network Intrusion Detection

Network intrusion detection systems often use signature-based matching (IDS) mechanisms. Using ML-based classifiers to detect threats is a more reliable approach, as signature-based methods can only find attacks that are already known. Due to the robustness of the attacks, a single ML classifier will not be able to identify all types of attacks, especially the more unusual ones. Therefore, a hybrid method that targets each type of attack separately might give us better results. Its goal is to combine the strengths of different individual classifiers. Another key issue is to use feature selection methods to select only relevant features, otherwise, we might face the problem of a high false positive rate. Our goal is to offer a hybrid strategy specifically created to solve the above problems.
Multi-factor Assymetric Key Encryption (MAKE) for Mutual Authentication in IoMT

Digital health, smart hospitals, and other uses of the Internet of Things (IoT) generate enormous amounts of heterogeneous data. Sensor nodes of IOT are incorporated with medical apparatus so that medical staff may continuously check patients' health and appliances. However, due to security flaws, an unauthorized person might access patient health data or manage the IoT nodes linked to their bodies, leading to results that were previously unheard of. Because wireless channels are used as a communication medium, IoT presents a number of risks to the security and privacy of IoT networks. We proposed a multifactor-oriented protocol which provides mutual authentication for transmission of data between patient and doctor by maintaining secrecy among smart card, sensor node, server, gateway and IoMT devices. We use the concept of assymetric key, XOR operation and the fuzzy extractor for better and more secure encryption. We proved the protocol formally by BAN logic and informally and then finally calculated the computational cost.
Applying Neural Network to Assess Application User Experience

The paper describes the approach to obtain an objective user assessment of the application user interface and the process of working with it in real time using convolutional neural network. The neural network is trained and the emotional assessment and level of user fatigue when working with the application are determined. It is assumed that the recognition of emotions, fatigue and concentration will be the best solution for high accuracy of assessment and maximum audience coverage.
Proving Information Inequalities and Identities With Symbolic Computation

Proving linear inequalities and identities of Shannon’s information measures, possibly with linear constraints on the information measures, is an important problem in information theory. For this purpose, ITIP and other variant algorithms have been developed and implemented, which are all based on solving a linear program (LP). In particular, an identity <inline-formula> <tex-math notation="LaTeX">$f = 0$ </tex-math></inline-formula> is verified by solving two LPs, one for <inline-formula> <tex-math notation="LaTeX">$f \ge 0$ </tex-math></inline-formula> and one for <inline-formula> <tex-math notation="LaTeX">$f \le 0$ </tex-math></inline-formula>. In this paper, we develop a set of algorithms that can be implemented by symbolic computation. Based on these algorithms, procedures for verifying linear information inequalities and identities are devised. Compared with LP-based algorithms, our procedures can produce analytical proofs that are both human-verifiable and free of numerical errors. Our procedures are also more efficient computationally. For constrained inequalities, by taking advantage of the algebraic structure of the problem, the size of the LP that needs to be solved can be significantly reduced. For identities, instead of solving two LPs, the identity can be verified directly with very little computation.
Decision Boundary Expansion For Open Set Fault Diagnosis

The domain adaption (DA) based on deep learning has achieved great success in cross domain unsupervised fault diagnosis. In general, it assumes that the label sets of training data (source domain) and test data (target domain) are consistent. However, in practice, the test data usually include unknown samples that are not observed in the training data due to unpredictable fault modes in the test phase. Therefore, the open set fault diagnosis (OSFD) algorithm where the label set of training data is a part of the label set of test data came into being. OSFD aims to identify unknown fault modes in the target domain and align the source domain and target domain to identify known fault modes. However, most previous studies directly align the source domain and target domain without considering the unique information of each class, which will make the decision boundary of the classifier indivisible. In this paper, we expand the decision boundary for OSFD from two aspects. First, the decision boundary between the unknown samples and all known classes is expanded, i.e., the unknown samples are pushed away from the decision boundary. Then, the decision boundary between all known classes is expanded, i.e., to align the known classes more accurately. On the twin-spool engine experiment, our method can precisely identify unknow fault modes and accurately classify known fault modes compared with previous methods, and the effectiveness of the method was verified by feature visualization and ablation study.
Dynamic Application Placement Technique for Intelligent Heterogeneous Fog Environment

Fog computing has gained immense popularity as it brings computing and processing close to the user’s devices and harnesses the system’s capability. However, the fog nodes are heterogeneous with different capacities and capabilities, resulting in many challenges including optimal placement of the application modules over the fog nodes and effectively mapping the nodes with the required resources for the tasks. Therefore to tackle these challenges, this paper proposes a technique for application module placement in a fog environment with heterogeneous nodes while optimizing bandwidth consumption and resource utilization. The proposed application placement technique uses deep Q-learning to predict the pattern of incoming requests and Markov Chain is used for modelling. OpenAI Gym framework is used for the simulation. Low-intensive and time-sensitive applications are executed on the appropriate fog nodes that are close to user’s device requesting the service. From the simulation results, it is observed that proposed technique outperforms the existing algorithms such as First Come First Serve and Fuzzy based algorithms, considering the fog environment’s dynamicity, mobility, and heterogeneity characteristics.
Legal Data Assistive Tool Using Deep-Learning

The proposed work aims to develop a legal document summarization tool using abstractive summarization techniques, with a focus on creating informative and concise summaries of legal documents. Modern deep neural network based pretrained models such as Distilled Bi-directional and Auto-regressive Transformer(DistillBart), Legal Pegasus, and Legal Longformer Encoder-Decoder(LED) are used to deal with the problem of low training dataset availability. Various sentence ranking approaches such as keywords, Parts Of Speech(POS) tagging, chunking based approach, and the position of sentences in the document and sentence centrality based on similarity are used to deal with the issue of large size of legal judgements. To improve informativeness of summary, focus is on entities such as important dates, concerned people, and referred acts in the legal case. The performance of different models employed for summarization are evaluated using evaluation metrics such as Bilingual Evaluation Understudy(BLEU), Precision, Recall, F1 Score, and Recall Oriented Understudy for Gisting Evaluation(ROUGE). The proposed system is a valuable tool for legal professionals to quickly understand the main points of a legal document and make informed decisions.
Fox-Face Compound Reconfigurable Antenna for Wireless Systems

A compact multiband Fox-Face compound reconfigurable antenna is proposed in this paper using two PIN diodes. On switching the states of the PIN diodes, the reconfigurable antenna resonates at multiple bands, having one band in each case for which the bandwidth is highest and is considered as the primary band. Primary frequency band is reconfigurable from 5.5GHz (used in 5G services) to 6.7GHz (used in digital high-capacity fixed point-to-point links), while a secondary band is present at around 9GHz (used in satellite and military applications) in all the four configurations possible in the design.
A Closed-Loop Electrophysiological Hardware Prototype to Estimate and Control Neuronal States

Neurons are considered to be the most basic structural and functional units of the nervous system. The functions of brain are closely related to the firing activity of neurons. However, a large number repetitive experiments are required to explore the target-related nerve system response in traditional electrophysiological experiments. The application of the “closed-loop” paradigm to electrophysiological experiments can obtain the desired stimulation signal easily, but the physiological experimental environment is harsh and there are ethical problems. Model-based simulation on the software platform can break through the above limitations, but it is difficult to reproduce the analog signals acquisition and estimation of physiological experiment. Therefore, this paper proposed a closed-loop electrophysiology hardware prototype (CLEHP), which capable of realizing real-time simulation of neuroelectrophysiological experiments. The Hodgkin-Huxley model and unscented Kalman filter is implemented on the CLEHP, and the results show that the CLEHP is able to generate neuronal electrophysiological activity accurately and to estimate and control the state of neurons in real time. More models and algorithms can be implemented on platform that based on CLEHP, which may promote the research on neural mechanisms and neural closed-loop therapy.
ECSTATIC: An Extensible Framework for Testing and Debugging Configurable Static Analysis

Testing and debugging the implementation of static analysis is a challenging task, often involving significant manual effort from domain experts in a tedious and unprincipled process. In this work, we propose an approach that greatly improves the automation of this process for static analyzers with configuration options. At the core of our approach is the novel adaptation of the theoretical partial order relations that exist between these options to reason about the correctness of actual results from running the static analyzer with different configurations. This allows for automated testing of static analyzers with clearly defined oracles, followed by automated delta debugging, even in cases where ground truths are not defined over the input programs. To apply this approach to many static analysis tools, we design and implement ECSTATIC, an easy-to-extend, open-source framework. We have integrated four popular static analysis tools, SOOT, WALA, DOOP, and FlowDroid, into ECSTATIC. Our evaluation shows running ECSTATIC detects 74 partial order bugs in the four tools and produces reduced bug-inducing programs to assist debugging. We reported 42 bugs; in all cases where we received responses, the tool developers confirmed the reported tool behavior was unintended. So far, three bugs have been fixed and there are ongoing discussions to fix more.