Miracle-dz/UniFAR-FTUs huggingface dataset:
Distributed Real-Time Workflow Scheduling for Collaborative Operation of Heterogeneous Industrial Equipment

In modern industrial environments, the collaborative operation of heterogeneous industrial equipment is crucial for enhancing production efficiency and resource utilization. However, achieving efficient workflow scheduling and cooperative actions necessitates addressing challenges related to distributed environments, heterogeneity, and realtime requirements. To tackle this issue, this paper addresses the workflow scheduling problem by representing scheduling states with disjunctive graphs. We treat task selection and component allocation as a combined decision and introduce an improved PPO algorithm, termed the PPOM algorithm. This algorithm incorporates an action mask into the traditional PPO framework to indicate valid or invalid actions for each state. By using the action mask, the PPOM algorithm proves to be more stable and practical in real-world applications, enhancing its convergence speed and adaptability. Experimental results show that the proposed PPOM algorithm achieves a reasonable improvement in efficiency compared to priority dispatching rules (PDRs) and other deep reinforcement learning (DRL) methods.
Research on the Role of OSS/OSH Platforms for Preliminary Simulation of UAVs: Demonstration with Case Studies

The dramatic evolution of the world of technology, particularly the world of autonomous vehicles and robots, suggests a complex, advanced, and sophisticated future generation of unmanned vehicles. This development includes software and hardware fields, which complement each other. This contribution analyzes the most commonly available and widely used Open-Source Simulation (OSS) platforms such as (jMAVSim, FlightGear, X-Plane, UE4Sim, AirSim, Gazebo, MORSE, and Webots) suitable for learning and scientific research environments. In addition, the Open-Source Project (OSP) platforms are described. The technical data of the Pixhawk and CUAV FC (Flight Controller) series have been reviewed. This work presents experimental case studies based on the AirSim simulation platform, the Rider platform, and the Pixhawk autopilot board. The results included conducting several virtual flights with the integration of the autopilot board into virtual simulated environments with the ability to monitor sensors, battery levels, and motors. Preliminary results showed reliable performance, good stability, and high responsiveness, with low cost and no risk of flight test. This work concludes that open-source platforms can be considered as a suitable approach for the research, development, and education of UAV systems. It is worth noting the importance of the simulation process prior to conducting the actual testing, especially in the field of aviation where risk ratios and sudden errors are higher. In addition, virtual simulation environments are valuable learning platforms in academia and industry.
A Comparative Study for Pneumonia Diagnosis Useing the Deep Learning Techniques

Pneumonia, a respiratory infection that causes inflammation of the air sacs in one or both lungs, is a significant contributor to mortality worldwide. Timely detection of acute pneumonia is crucial as it can be fatal without prompt medical intervention. Radiographic imaging, specifically X-ray imaging, is commonly used for diagnosing pneumonia. This study proposes a technique based on deep learning algorithms, including Convolutional Neural Network (CNN), DenseNet-121, and VGG-16, for analyzing X-ray images to detect pulmonary inflammatory diseases. This study was trained on a database comprising 5,216 X-ray images. The results of the study demonstrate that the CNN algorithm outperforms the DenseNet-121 and VGG-16 algorithms in terms of accuracy. Consequently, a program employing the CNN algorithm was developed to assist healthcare professionals in the diagnosis of pneumonia.
Hysteresis Amplifier Model to Eliminate Multi-switching on IGBT Work

The occurrence of multi-switching on an isolated gate bipolar transistor (IGBT) function can cause significant power dissipation and disrupt the power network. To overcome this problem, this paper designs a hysteresis amplifier model to reduce multi-switching in IGBTs. This software is used to simulate and analyze the performance of the hysteresis model amplifier. The hysteresis amplifier design is used to induce a hysteresis effect on the IGBT control signal. This effect produces an output frequency of 50 Hz with a 50% duty cycle. The working mechanism of this design includes the formation of a hysteresis window on the IGBT control signal, +2.00 V to +2.52 V. If the design result of the control signal is at least +2.50 V to +2.48 V, the IGBT enters the active state when Vout1 reaches +3.51 V. The design results in the control signal reaching a maximum value of +2.50 V to +2.51 V. The IGBT transitions to the active state when the voltage at Vout2 reaches a threshold of +3.51 V. The automatic implementation of the hysteresis window effectively reduces the occurrence of multi-switching by ensuring that a switch event occurs when the control signal is within the specified hysteresis window range. The current transformer detects changes in current through the variable resistor when Vin +2.00V to Vin +2.52 V. By understanding the characteristics and implementation of the model, it is possible to formulate intelligent control strategies aimed at increasing the efficiency and stability of system power through the use of IGBT.
A novel scattering-type THz microprobe with integrated source and detector for contact-free, high-speed surface imaging at sub-μm-resolution

Terahertz (THz) Time Domain Spectroscopy (THz-TDS) provides an interesting set of possibilities for contactless wafer inspection as many used materials contain resonant features in their refractive indices within the THz-spectrum. It can be used for example to analyze electrical properties, such as sheet resistance. Even though there is a large set of advantages for THz-TDS, the wavelength of THz radiation is often in the same or higher range as geometric features of interest. The limits for the spatial resolution of THz measurements are therefore given by the THz waves while scattering effects are not limited by the wavelength. To make use of the THz scattering effect to achieve spatial resolutions in the nanometer scale, a known approach is the scattering-type Scanning Near-field Optic Microscopy (s-SNOM) is used. In s-SNOM a sharp oscillating tip, similar to the ones used in atomic force measurements (AFM), is positioned close to the surface and is radiated with THz waves to cause the scattering effect. A detector then measures those scattering waves. One major hurdle for implementing s-SNOM is the complex setup required for alignment and operation since the THz emitter, the scattering tip and the THz detector are separated. This paper presents a method where a new THz probe antenna design provides the possibility to measure THz scattering and allows high spatial resolutions by combining those components into a probe tip. As a result, not only high spatial resolutions can be achieved but also an oscillation tip can be omitted.
Environment Sensing With Beam Sweeping and Non-Uniform Pixelation in Wireless Communication Systems

In this paper, we consider the problem of integrated sensing and communication (ISAC) system design over wireless networks. Specifically, the mobile station (MS) in the ISAC system sends uplink communication signal beams to the base station (BS), and the BS accomplishes the environment sensing by processing the propagation gain from received beams. Since the uniform discretization of the environment scenario has inaccurate descriptions of the BS/MS position and object occlusion relationship, we propose a non-uniform pixel discretization method. According to the location of the transceiver and the direction of beams, we discretize the environment into layered non-uniform pixels, which reflect the occlusion relationship between objects in the environment. Based on the sparse features of environmental scatterers, we propose an environment sensing algorithm based on compressed sensing and approximate message passing. The proposed algorithm achieves accurate environment sensing by iteratively removing occlusion interference. At the same time, with the continuous sweeping of the transmitting and receiving beams, the environment sensing results gradually improve. Finally, simulation results demonstrate the effectiveness of the proposed ISAC system design and algorithm.
Active Face Recognition Through View Synthesis

Active vision exploits the ability of robots to interact with their environment, towards increasing the quantity / quality of information obtained through their sensors and, therefore, improving their performance in perception tasks. Active face recognition is largely understudied in recent literature. In this paper, we propose an active approach that utilizes facial views produced by facial image rendering. The robot that performs face recognition selects the best candidate rotation around the person of interest by simulating the results of such movements through view synthesis. This is achieved by passing to the robot's face recognizer a real world facial image acquired in the current position, generating synthesized views that differ by $\pm\theta^{\circ}$ from the current view. Then, it decides, on the basis of the confidence of the recognizer, whether to stay in place or move to the position that corresponds to one of the two synthesized views, so as to to acquire a new real image. Experimental results in two datasets verify the superior performance of the proposed method compared to the respective static approach and an approach based on the same face recognizer that involves face frontalization with synthesized views.
First proper motions of thin dust ﬁlaments at the Galactic center (cid:1)

Context. L (cid:2) -band (3.8 µ m) images of the Galactic center show a large number of thin ﬁlaments in the mini-spiral, located west of the mini-cavity and along the inner edge of the Northern Arm. One possible mechanism that could produce such structures is the interaction of a central wind with the mini-spiral. Additionally, we identify similar features that appear to be associated with stars. Aims. We present the ﬁrst proper motion measurements of the thin dust ﬁlaments observed in the central parsec around SgrA* and investigate possible mechanisms that could be responsible for the observed motions. Methods. The observations have been carried out using the NACO adaptive optics system at the ESO VLT. The images have been transformed to a common coordinate system and features of interest were extracted. Then a cross-correlation technique could be performed in order to determine the o ﬀ sets between the features with respect to their position in the reference epoch. Results. We derive the proper motions of a number of ﬁlaments and 2 cometary shaped dusty sources close (in projection) to SgrA*. We show that the shape and the motion of the ﬁlaments does not agree with a purely Keplerian motion of the gas in the potential of the supermassive black hole at the position of SgrA*. Therefore, additional mechanisms must be responsible for their formation and motion. We argue that the properties of the ﬁlaments are probably related to an outﬂow from the disk of young mass-losing stars around SgrA*. In part, the outﬂow may originate from the black hole itself. We also present some evidence and theoretical considerations that the outﬂow may be collimated.
Computation Offloading and Resource Allocation in IoT-Based Mobile Edge Computing Systems

With the rise in popularity of artificial intelligence (AI) and internet of things (IoT) technologies, advanced AI technologies have been widely applied to support delay/time-sensitive tasks of IoT-based user equipment (UE) in IoT systems, which allows IoT-based UEs to offload their tasks to a remote fog, edge or cloud computing server. To reduce the consumption of delays (which may include transmission delays, queueing delays, and processing delays) while efficiently allocating the computation resource at a remote server, an efficient offloading decision solution needs to be proposed. In this paper, an IoT-based network system consisting of two layers will be proposed, where The bottom layer is the IoT-based UE layer, which includes multiple IoT-based UEs, and the top layer is the mobile edge computing (MEC) layer, which includes an edge node embedded with the base station. We propose a double Q-Learning-based offloading decision and computation resource allocation optimisation algorithm (DQOCA), which aims to jointly optimise the offloading decisions among all IoT-based UEs and optimise computation resource at the MEC server to reduce the maximum delay consumption among all IoT-based UEs. Simulation findings show that, in comparison to benchmarks (i.e., local processing and edge processing schemes), our proposed approach greatly minimises the maximum delay consumption.
Optimal Radio Labeling of $C_{4}\boxtimes P_{n}$

The graph is generally used to model the channel allocation of wireless communication networks, and the distance parameter of the vertices in the graph is used to analyze the intensity of channel interference between radio stations. Channel allocation is the process of assigning appropriate channels to each station in the same network based on distance constraints to prevent the problem of interfering with each other's communications, and the concept of radio labeling is an emerging concept derived from converting the channel allocation problem into graph labeling problem. In this paper, we focus on the topology structure of the strong product of 4th order circle and nth order path ($n\geq 7$), and use it to simulate the channel allocation of a wireless communication network with the same structure. The consistency results of the upper and lower bounds of its radio labelings are obtained, and the optimal radio labeling is determined.
Vehicle Classification in Intelligent Transportation Systems Using Deep Learning and Seismic Data

Intelligent transportation systems have become increasingly important for efficient traffic management and road safety. Vehicle classification is a fundamental task in these systems, enabling various applications such as traffic monitoring, congestion management, and accident prevention. Traditional methods for vehicle classification heavily rely on visual or sensor-based data, such as images or radar signals. However, these methods may encounter limitations in adverse weather conditions, poor lighting, or occlusion scenarios. To address these limitations, this paper introduces a novel approach for vehicle classification using seismic data, which captures the vibrations generated by vehicles and is less susceptible to environmental factors. The proposed approach leverages the fractional wavelet domain to extract both time and frequency signatures of vehicles from the seismic data effectively. These signatures are then used to train a recurrent neural network that captures the temporal features of the input, thereby facilitating vehicle classification. Additionally, the proposed approach incorporates various modules to improve the generalization and robustness of the deep model against noise. The implementation of the proposed approach on realistic data demonstrated its ability to classify vehicles with an accuracy of 98%, improving upon the state-of-the-art techniques.
Topology Optimization of Piezoelectric Structures : Micro-Actuators and Energy Harvesters

This paper reviews the primary research efforts conducted within the AS2M department of the FEMTO-ST institute, focusing on topology optimization of piezoelectric structures. The paper primarily highlights the principle and the possibilities offered by topology optimization with a specific emphasis on the SIMP approach (Solid Isotropic Material with Penalization). Then, the design processes of piezoelectric micro-actuators and energy harvesters are described. The optimized piezoelectric structures are presented and the improvements over classical designs are assessed. Finally, the paper discusses the feasibility and the potential of multi-material topology optimization.
Plug-and-Play Reconstruction with 3D Deep Prior for Complex-Valued Near-Field MIMO Imaging

Near-field radar imaging systems are used in a wide range of applications, such as medical diagnosis, through-wall imaging, concealed weapon detection, and nondestructive evaluation. In this paper, we consider the inverse problem of reconstructing the three-dimensional (3D) complex-valued reflectivity distribution of the near-field scene from the sparse multiple-input multiple-output (MIMO) array measurements. Using the alternating direction method of multipliers (ADMM) framework, we formulate this problem by exploiting regularization on the magnitude of the complex-valued reflectivity distribution. We then provide a general expression for the proximal mapping associated with such regularization functionals operating on the magnitude of the complex-valued unknown. By utilizing this expression, we develop a computationally efficient plug-and-play reconstruction method that involves simple update steps both with analytical and deep priors. We illustrate the reconstruction performance of our approach with a 3D deep prior on a synthetic dataset. We also compare the result with the classical back-projection method and magnitude-total variation. Our results demonstrate that significant performance improvement can be achieved with learned 3D priors.
Design of an Asynchronous BCI Based on a Facial Expression Paradigm for the Remote Control of Robotic Systems

In this paper, an asynchronous electroencephalogram (EEG) Brain-Computer Interface (BCI) based on the facial expression paradigm was designed for the control of the Smart Video Car robot. The proposed BCI was designed to address the problem of lengthy and tedious BCI experiments, which is one of the limitations to their general public use. To minimize the duration of the Subjects' training session while ensuring a good classification of the EEG data, an approach based on the “curse of dimensionality” theory was used. The designed BCI achieved offline accuracy in the range of 80.30-99.76% and performed asynchronous online mode without false detection, which means a 100% accuracy during the robot control thanks to the specified thresholds. The experimental results show that a good trade-off can be found between the comfortability of the experiment for the Subjects and the need to have a sufficient amount of training data for satisfactory classification.
Raspberry Pi Implementation of The Wilson-Cowan Neural Network With Chemical Synapse

Information transmission in living things occurs through synapse connections between neurons and the transfer of ions from one neuron to another. Synapses can be Electrical and Chemical. Transmission at electrical synapses is direct and very fast. Unlike chemical synapses, it does not inhibit the other neuron. Chemical synapses are more flexible, versatile, and more active in transmitting complex information than electrical synapses. The Wilson-Cowan (WC) neuron model explains neural activity in the Neocortex with mean excitatory and inhibitory interaction. It is essential because it has a simple mathematical structure and can explain the activities of a large neural population. While there are models of electrically coupled WC neural populations in the literature, there are no studies on a WC neural population with the advantages of chemical coupling. This paper presents a simple structured chemical coupling model WC neural population that does not add any new variables to the system. The standard deviation method provides the synchronous and asynchronous behavior of this network structure, which is one of the stability control approaches. In addition, electrical and chemically coupled WC neural population; The hardware solution has been realized on a standard equipped Raspberry Pi (RPi), which has high configurability, can solve complex mathematical expressions easily and quickly, has no memory limitation, low thermal noise sensitivity, low-cost production process. In this respect, our study provided support to the practice field as well as its theoretical innovation. In addition, the integration of chemical coupling into the WC neuron is the first in that the coupling does not have computational difficulties compared to other chemical coupling models and that synchronous-asynchronous behaviours are realized over RPi.
Enhanced Stock Movement Prediction with Event Graph and Dynamic Sentiment Analysis

Stock movement prediction is of paramount importance in financial markets, informing sound investment strategies. However, traditional numerical analysis often falls short due to market volatility and the inherent challenge of modeling dynamic change. We propose a Dynamic Event and Sentiment Interaction Graph Attention Network (DESIGN) method integrating numerical characteristics, news and comment text, with the added dimension of an event graph tracking the chain of events from news text. The method follows a multi-step approach for stock movement prediction, it starts by classifying the news text and then performs reasoning through the event graph to obtain event-inference-based indicators. These indicators are then fused with the ones derived from the raw numerical data, resulting in evolution features. Then, the method classifies the sentiment of comment text and dynamically constructs a stock relationship graph based on this result. Finally, a graph neural network is employed for prediction. Extensive experiments verify DESIGN's superior performance in stock movement prediction.
On-Line Chord Recognition Using FifthNet with Synchrosqueezing Transform

Automatic chord recognition is a fundamental and important task in music information processing. FifthNet is a recently proposed chord recognition framework based on a deep neural network (DNN). Its aim is to reduce the computational and memory loads by taking advantage of knowledge on music signals. Since FifthNet achieved the state-of-the-art performance and requires less computational resource compared to the other DNN-based methods, it seems suitable for real-time applications. However, the original FifthNet cannot be directly used in real-time because it applies the spectral reassignment method in its feature extractor. The reassignment method requires some look-ahead frames that results in unavoidable latency. In this paper, we propose to replace the reassignment method of FifthNet with the synchrosqueezing transform to reduce the amount of required look-ahead frames and the computational requirement. Our modification makes FifthNet on-line and removes the obstacle towards real-time execution. In addition, the experimental result shows that the accuracy of chord recognition can be improved by the proposed modification.
Deep Learning in Clinically Significant Prostate Cancer Classification via Biparametric MRI Sequences

Accurate detection and classification of clinically significant prostate cancer (csPCa) play a pivotal role in determining optimal treatment strategies. Biparametric MRI (bpMRI) employs diverse imaging sequences to enhance diagnostic accuracy. This paper aims to explore the potential of deep learning frameworks, specifically DenseNet-201 and ResNet-50, in distinguishing between clinically significant (csPCa) and non-significant (non-csPCa) prostate cancer using various imaging sequences from biparametric MRI. The methodology involves an in-depth analysis of axial T2-weighted imaging (T2W), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) mapping using the aforementioned deep learning frameworks. Our findings demonstrate that among the studied biparametric MRI sequences, axial T2W exhibits the highest accuracy in csPCa classification, as indicated by both classification accuracy and the area under the receiver operating characteristic curve (AUC).