Miracle-dz/UniFAR-FTUs huggingface dataset:
Specific tools for studying the optical response of heterogeneous thin film layers

Abstract. The extraordinary progresses in the design and realization of structures in inorganic or organic thin films, whether or not including nanoparticles, make it possible to develop devices with very specific properties. Mastering the links between the macroscopic optical properties and the optogeometrical parameters of these heterogeneous layers is thus a crucial issue. We propose to present the tools used to characterize and to model thin film layers, from an optical point of view, highlighting the interest of coupling both experimental and simulation studies for improving our knowledge on the optical response of the structure. Different examples of studies are presented on copper indium gallium selenide, perovskite, P3HT:ZnO, PC70BM, organic layer containing metallic nanoparticles, and colored solar cells.
Characteristics of vibration in magnetically levitated trains subjected to crosswind

An electromagnetic suspension system-type (EMS-type) magnetically levitated vehicle (Maglev) maintains the airgap between the guiderail and the electromagnet by controlling the electric current through the levitation controller and runs with the help of a linear induction motor. ECOBEE, an EMS-type Maglev, is designed for the purpose of urban transit and must run on a curved guideway with a small radius of curvature. However, while the EMS-type Maglev controls the vertical airgap using the levitation force of the electromagnet, in the case of the lateral direction, a guidance force, which is determined by the levitation force through the intensity of lateral displacement, passively controls the lateral displacement. However, when an excessive lateral displacement is triggered by a disturbance that is greater than the guidance force, an antiaberration skid comes into contact with the rail. Therefore, the Maglev running on a curve with a small radius should maintain the airgap without touching the rail when turning directions on a curved rail, while the vibrations of the vehicle caused by external factors, such as entering the circular curve from the transition curve, or a crosswind, can affect the levitation stability. Therefore, in order to secure the levitation stability of the electromagnet while also improving the curving performance of the vehicle, inserting a lateral damper between the cabin and the bogie has been suggested. In the present study, by using an integrated model of the Maglev system developed by multibody dynamics, the influence of a crosswind as the train runs along a curve and the effect of the lateral damper were analyzed.
An unsteady MHD Maxwell nanofluid flow with convective boundary conditions using spectral local linearization method

Abstract The main focus of this study is on unsteady Maxwell nanofluid flow over a shrinking surface with convective and slip boundary conditions. The objective is to give an evaluation of the impact and significance of Brownian motion and thermophoresis when the nanofluid particle volume fraction flux at the boundary is zero. The transformed equations are solved numerically using the spectral local linearization method. We present an analysis of the residual errors to show the accuracy and convergence of the spectral local linearization method. We explore the effect of magnetic field and thermophoresis parameters on the heat transfer rate. We show, among other results, that an increase in particle Brownian motion leads to a decrease in the concentration profiles but concentration profiles increase with the increasing value of thermophoresis parameter
Evaluating performances of various CNN architectures for multi-class classification of rotten fruits

In recent years, Artificial Intelligence (AI) has been at the crux of many advancements across different industries such as E-Commerce, Finance, and Healthcare. The introduction of new Machine Learning techniques in the agri-food industry could become a turning point. The quality of the produce is one of the major factors that drives this industry, and it plays an important role in consumer consumption thereby affecting the sale of the produce. For a country like India, which heavily depends on agriculture, the introduction of modern methods in agriculture is vital for increasing efficiency and thus increasing the amount and quality of produce. AI-driven autonomous systems could cut down the manual labor which will hence reduce processing times and can have a huge impact on processing expenditure as well as sales. Deep Learning along with Computer Vision can be used to analyze the quality of fruits and vegetables. It can also be used to grade the goods according to their freshness and can even identify rotting fruits and vegetables. Traditionally, manual labor is used to perform the above-mentioned tasks but with the introduction of the proposed technology, we can cut down the costs and reduce the processing time. We plan to implement various pre-trained CNN architectures using Transfer Learning to implement multi-class classification of rotten fruits. Finally, we use performance speed and accuracy as the main metrics to compare the models and showcase their usefulness in this domain.
Examination of the possibility of adapting the PI algorithm

The article examines what kind of PI control problems can occur when replacing a PLC (Programmable Logic Controller). In industry, it happens that the PLC breaks down or is out of date, so it has to be replaced. The research shows how to create the algorithmization of the PI (Proportional Integral) algorithm written in the FBD (Function Block Diagram) programming language of the individual manufacturer. After tuning the algorithm, we can transfer the tuned parameters and the algorithm to the other PLC. It examines whether programs written in different PLCs can be specifically adapted by PI control into another controller. It reveals what functional and quality differences are experienced during the examination.
A Novel Framework for Efficient Offloading of Communication Operations to Bluefield SmartNICs

Smart Network Interface Cards (SmartNICs) such as NVIDIA’s BlueField Data Processing Units (DPUs) provide advanced networking capabilities and processor cores, enabling the offload of complex operations away from the host. In the context of MPI, prior work has explored the use of DPUs to offload non-blocking collective operations. The limitations of current state-of-the-art approaches are twofold: They only work for a pre-defined set of algorithms/communication patterns and have degraded communication latency due to staging data between the DPU and the host. In this paper, we propose a framework that supports the offload of any communication pattern to the DPU while achieving low communication latency with perfect overlap. To achieve this, we first study the limitations of higher-level programming models such as MPI in expressing the offload of complex communication patterns to the DPU. We present a new set of APIs to alleviate these shortcomings and support any generic communication pattern. Then, we analyze the bottlenecks involved in offloading communication operations to the DPU and propose efficient designs for a few candidate communication patterns. To the best of our knowledge, this is the first framework providing both efficient and generic communication offload to the DPU. Our proposed framework outperforms state-of-the-art staging-based offload solutions by 47% in Alltoall micro-benchmarks, and at the application level, we see improvements up to 60% in P3DFFT and 15% in HPL on 512 processes.
Automatic Music Score Difficulty Classification

Learning a musical instrument is challenging in many different aspects. One important aspect is the motivation of the music students. One common practice to keep music students engaged is to use songs that the students like. However, identifying an appropriate music score for the music students that is compatible with the specific skill level and music taste of each student can be a challenging task. Defining the difficulty of each score manually is a complex task, as it requires theoretical knowledge and mastery of a particular musical instrument and music theory. In this context, the automatic music score difficulty classification task can help music teachers and students by automatically classifying music scores according to their difficulty. The main contribution of this work is to evaluate the use of two approaches based on the musicXML files and different classification algorithms for this task concerning three different music instruments, namely the violin, the piano and the acoustic guitar. Our experiments show interesting results for all the musical instruments.
UAV Remote Sensing applications and current trends in crop monitoring and diagnostics: A Systematic Literature Review

Crop monitoring and diagnosis are crucial for efficient agricultural production, and unmanned aerial vehicle (UAV) Remote Sensing can assist in achieving this goal. This article offers an automated Systematic Literature Review (SLR) of UAV Remote Sensing for crop monitoring and diagnosis. This review analyzes the primary scientific applications and trends in this area using Deep Learning techniques to automatically select relevant articles and validate them through full reading. The SLR collected over 800 papers, of which 64 met the selection process. The articles selected by Deep Learning classifiers were successfully cataloged with high accuracy in pre-selecting articles for review. F1 scores of 93% were achieved in tests with unpublished examples for the classifier model. The review of the 64 primary studies reported a peak in UAV Remote Sensing applications in 2020, attributed to the increasing diffusion of precision farming practices with technological equipment. The UAV Remote Sensing application objectives included crop monitoring, pest and disease detection, yield prediction, and plant nutrition. Artificial Intelligence, particularly Machine Learning and Deep Learning, are widely used for UAV Remote Sensing analysis. The NDVI is the most applied vegetation index for crop condition assessment and monitoring. The proposed solution for automating the literature selection process for precision agriculture-related scientific articles can be used in other areas that require extensive literature review.
Heterogeneous Memory Allocation Scheme for Batch Jobs with Intel Optane DC Persistent Memory and DRAM

It is a promising approach to include Non-Volatile Memory (NVM) and DRAM together as the main memory to provide huge memory space for large objects and guarantee short access latency for frequent writes and reads. To provide feasible and suitable memory allocation for all applications on a sever, our solution analyzes applications to derive the read-write ratio, memory footprint, and access pattern of each application. Heterogenous memory allocation scheme is then developed to reduce the makespan of batch jobs.
Edge Computing supporting On-Time Diagnostics of Industrial Motor Drives

In the industrial context, the transformation of any process related information to a digital format affects the entire company, from administration to production and logistics (digitization). This paper focuses on Edge Computing, which consists in extending the scope of a Programmable Logic Controller (PLC) from the standard coordination and control of an industrial drive, to include a Condition Monitoring process. The latter can be implemented without interference with the regular real time control operation of the plant, and still a number of relevant data are detected and processed on-time, both to monitor the quality of the operation, and to perform Diagnostics Algorithms. The software architecture and some relevant experimental results are reported in the paper
Predictive Analytics System for Stock Data: methodology, data pre-processing and case studies

Predictive analytics system is a pillar of science for forecasting future trends in vast amount of data. The aim of this research is to make decision buying or selling trend the stocks based on the calculated value of Moving Average (MA) method and Long Short Term Memory (LSTM) model for historical data. This paper presents a big data analytics framework which contains data collection, pre-processing and describe a correlation between various stock market datasets. Moreover, the proposed architecture overcomes multiple challenges including extracting large amount of data, collecting, transformation, storing, analytics and visualization in stock data using machine learning tools, tensor flow and pyspark framework. The system achieves the prediction accuracy with 95.82 % by using LSTM model and it also gives the correlation 54% between Monarch staffing (MSTF) stock and Alphabet (GOOG) stocks by using MA method. However, this work establishes a fundamental concepts for students, researchers and industrial practitioners on how to analysis and predictive stock data movement using technical analysis and neural network in trading algorithm.
System of Systems for Distributed Disaggregated Communications via Reinforcement Learning and Backpressure (D2CRaB)

This paper focuses on effectively communicating within distributed and disaggregated multi-domain battlespace operational system of systems (SoS) environments. To do so, the individual participating communications systems must be adaptive in response to ever-changing on-mission events such as, network congestion (e.g., degraded comms links and excessive network traffic), enemy interference (e.g., jamming, cyber-attacks), line of sight degradation (e.g., weather conditions). These challenges cause traditional communications systems networks to drop packets, which they presently do according to pre-specified Quality of Service (QoS) policies. However, these QoS policies alone do not actually mitigate the congestion problem because the impacted streams employ reliable communications protocols, such as TCP, that require dropped packets to be retransmitted over the same route. This approach can make the congestion problem even worse and waste the valuable bandwidth. The approach presented here introduces the novel System of Systems (SoS) for Distributed Disaggregated Communications via Reinforcement Learning (RL) and Backpressure (D2CRaB) to overcome the above stated limitations. D2CRaB does so in two ways: (1) by bridging route selection and congestion control via a new backpressure scheme and (2) by leveraging RL to enable the dynamic and continuous adaptation to changing network conditions. These new advancements, greatly extend the available bandwidth of the original congested communications system to that available of other communications systems within the multi-domain SoS. By so doing, congestion can be relieved, and packet drops can be greatly reduced.
Periodic solutions and homoclinic solutions for a Swift-Hohenberg equation with dispersion

We investigate the 1D Swift-Hohenberg equation with dispersion $$u_t+2u_{\xi\xi}-\sigma u_{\xi\xi\xi}+u_{\xi\xi\xi\xi}=\alpha u+\beta u^2-\gamma u^3,$$ where $\sigma, \alpha, \beta$ and $\gamma$ are constants. Even if only the stationary solutions of this equation are considered, the dispersion term $-\sigma u_{\xi\xi\xi}$ destroys the spatial reversibility which plays an important role for studying localized patterns. In this paper, we focus on its traveling wave solutions and directly apply the dynamical approach to provide the first rigorous proof of existence of the periodic solutions and the homoclinic solutions bifurcating from the origin without the reversibility condition as the parameters are varied.
Opinion dynamics with stubborn agents over a cycle

In the literature pertaining to opinion dynamics, stubbornness of agents has been accounted for in the Taylor’s model. Although stubbornness may potentially slow down the convergence to consensus or agreement, it may not necessarily be an undesirable feature in the evolution of collective opinions, particularly when noise may corrupt the dynamics of each agent’s opinion. Having few stubborn agents in the group can, in fact, make a network more resilient to misinterpretation and miscommunications, which may be viewed as additive noise. In this paper, we study the effect of stubborn agents and their locations in a cycle graph, on noise rejection, using H2 performance measures. It is analytically proved that in case of two stubborn agents, placing them farthest apart will be better than placing them side by side, for noise rejection.
A Deep Learning Model for Stock Price Prediction in Swing Trading

As deep learning emerges and achieves remarkable success in many application areas, this paper presents a deep learning model for stock price prediction based on Multi-Input LSTM (MI-LSTM). In addition to new neural network architecture, we also try to take advantage of human traders’ wisdom by including the values of some recognized technical indicators in the network input in addition to raw prices. Experimental results show that our model could achieve more than 10% loss reduction, promising in higher potential trading profits.
Cyber-Physical Design and Implementation of Service Restoration Framework for Islanded Power Systems

Cyber-physical energy systems with high penetration of distributed energy resources provide several grid support functions including black-start in islanded mode. In an inverter dominated power system, both grid-forming (GFM) and grid-following (GFL) inverters have a role to play in regulating system frequency/voltage and maintaining proportional power sharing through grid support functions. A self-sufficient power system can restore critical loads by forming microgrids (MGs) around black-start distributed generators (DGs). This work proposes a service restoration framework that adopts GFM inverters as black-start units and achieves load restoration in the context of dynamic MGs with automatic sectionalization and network reconfiguration. The proposed approach is structured as a two-layer framework consisting of an optimization and power systems simulation layer. Mathematical optimization models can only solve for the steady-state power equations. However, the associated dynamic transient response can cause instability in a weak power grid. Therefore, the optimization layer explicitly incorporates dynamic stability constraints developed from the power systems simulation layer. An optimal network reconfiguration sequence and power reference commands for DGs are generated during the restoration process. Moreover, a consensus based multi-agent distributed control is designed for coordination among GFM and GFL inverters. The approach is validated over a modified IEEE-123 node test feeder using real-time controller-in-the-loop simulation with OPAL-RT simulator.
The Efficient Traffic Sign Detection and Recognition for Taiwan Road Using YOLO Model with Hybrid Dataset

According to the trend of worldwide car sales have grown up, this cause may increase accidents on the road due to human error. The self-driverless car has been developed to solve this problem. One task of the self-driverless car is traffic sign detection and recognition (TSDR), which will help drivers notify the traffic sign installed on the road in advance. Taiwan roads have specific traffic signs, and no Taiwan traffic sign public dataset is available. In this paper, our proposed object detection method was experimentally performed using YOLOv5s6 and YOLOv8s models on three different datasets, as Tsinghua-Tencent 100K (TT100k), the self-created Taiwan traffic sign (TWTS), and the hybrid dataset, which combine the traffic scenes between TT100k and TWTS dataset. The output results from each dataset and each model, which is trained on the same parameter, will be compared to validate the proposed method. The experiment results’ comparison of the hybrid dataset between YOLOv5s6 and YOLOv8s models display the results of the mAP@. 5 is about 65% and 76.2%, respectively, which means the performance of the YOLOv8s is higher than the YOLOv5s6 when using hybrid dataset.
Resilient Control Algorithm for Wind-Hydro Based Distributed Generation System With Grid Synchronization Capability

A microgrid is key to an efficient and resilient power grid operation. Thus, in this paper, a robust control scheme is implemented namely a modified frequency locked loop (MFLL), proficient in eliminating the voltage and current fluctuations because of phase/frequency variation. Therefore, enhancing the consistency of the supply across local nonlinear load through a smooth transition from utility-interactive mode to islanded mode. It also achieves accelerated convergence and reduced steady-state error. MFLL is an easy control technique, with only one interfacing voltage source converter, which delivers a stable supply to critical loads during different operating modes. The implemented system has the main advantage of the capability of power-sharing between distributed generation (DG) systems while regulating the voltage at the DC link. It also provides power quality improvement through harmonics suppression; voltage regulation during contingencies like unbalance in load and compensation in reactive power at the common coupling point following the prerequisite of the system. Performance through the implemented control algorithm is brought within MATLAB 15b/Simulink platform and validated through experimental results. Performances are compared with already reported control strategies. Thus, simulated results comparison with existing work presents the competence of the implemented control strategy under dynamic conditions.
Embedded Machine Learning for 3D Indoor Visible Light Positioning via Optimized Fingerprinting

In this paper, a low power node featuring 3D indoor localization via Visible Light Positioning (VLP) and embedded Machine Learning (ML) is presented. The coordinates estimation is performed by a low-complexity shallow Neural Network (NN) running on board of a microcontroller and which approximates the regression model linking received light intensities and position in the workspace. The received signal strengths (RSSs) of the optical signals, coming from four LEDs chopped at unique frequencies and used as fixed anchors, are evaluated via fast Fourier transform (FFT). The generation of the datasets used to test and train the NN exploits a novel fingerprinting procedure which combines the simulation of the data through the canonical model for the light propagation and the acquisition of measurements in the workspace. Field tests performed in a $1 \mathrm{m}\times1 \mathrm{m}\times0.2\mathrm{m}$ volume prove an overall mean accuracy of about 3 cm with standard deviation of 1.5 cm and maximum error below 6 cm.