Miracle-dz/UniFAR-FTUs huggingface dataset:
Water in coesite and wadsleyite: quantification, incorporation mechanism and effect on the phase stability

25 Introduction.26 Experimental methods.27 Syntheses.27 FTIR spectroscopy.27 Secondary Ion Mass Spectrometry.29 Results.30 Syntheses.30 Hydrogen and Boron incorporation.31 Discussion.36 Conclusion.39 Acknowledgements.40 References.41 Chapter 2.44 “Location and quantification of hydroxyl in wadsleyite: new insights” Abstract.45. 45 Introduction.46 Experimental methods.49 Syntheses.49 Electron Microprobe.50 Raman spectroscopy.51 FTIR spectroscopy.52 Secondary Ion Mass Spectrometry.53 X-ray diffraction single crystal X-ray diffraction (SC-XRD). 54 Powder X-ray diffraction.55
Construction of Text-Based Traffic Sign Dataset and Research on Target Detection and Text Recognition

Autonomous driving relies on vehicles autonomously gathering semantic information from the traffic environment. The traffic sign recognition algorithm is pivotal in advanced driver assistance systems. Addressing challenges like small sign detection accuracy, vertical text recognition, and a lack of Chinese text-based datasets, we propose an improved YOLOv7 algorithm with BiFormer attention. This boosts small target detection. To counter BiFormer's speed impact, we prune the model for size. For text recognition, we improve the CRNN network for vertical text. Additionally, we create a Chinese text-based traffic sign dataset with annotations for both object detection and text recognition.
Edge Computational Integrated Improved Data Model for Adjustment and Restoration Strategy for Smart Grid Environment

In the current context, integrated distribution networks of the electricity grid to provide reliability during blackouts. Self-healing systems may keep functioning and reconfigure themselves in response to external breakdowns or attacks by using the efficient strategy of restoration. An intelligent decision-making framework is necessary to ensure the system as a whole can self-reconfigure in the face of external failures and attacks. Incorrect grid modifications can pose a significant threat to the power grid's security and cause delays in network traffic and packet loss. So, in this research, Edge Computational Integrated Improved Data paradigm (EC-IDM) to deal with failures and attacks in real time. By mitigating the consequences of security worries, packet loss, latency, and overcrowded networks, edge computing has greatly increased the smart grid's capacity for efficient operation and communication. Smart Grid's posterior intelligent model (SSPIM) integration enhances the system's functionality and productivity. This research introduces an EC-IDM that can detect blackout scenarios and automatically construct a restoration strategy in real time. In EC-IDM, an enhanced hierarchical coordination mechanism and a local object optimization method are combined to optimize failures and attacks in real time over many time and space axes. Differentiating features of EC-IDM include real-time data analysis, networked simulation, smart decision support, and a wealth of other useful features. The simulation results indicate that the recommended strategy achieves best in security concerns optimizes network congestion delay, and packet loss security, increases efficiency and performance compared to the other standard ways.
Deep Learning Model Based on TCN-LSTM for Projectile Initial Velocity Inversion of High-spin Projectiles

In the design process of high-spin projectile (HSP) shooting, it is necessary to obtain the accurate projectile initial velocity (PIV) with initial disturbances, for the subsequent external ballistics calculations and adjustment processes. Then, the PIV inversion model is proposed in this paper based on Temporal Convolutional Networks (TCN) and Long Short-Term Memory Networks (LSTM). And a database is also established by the 6-degree-of-freedom projectile dynamics model and external ballistics simulation with Runge-Kutta method. From the simulation results, the proposed TCN-LSTM model is compared with the BP model, LSTM model, and GRU model using the same dataset. It is noted that the TCN-LSTM model performs excellently PIV inversion, with a higher accuracy compared to the single-precision model.
Feature Extraction in Dermoscopic Images for Analysing Dryness Using Second Order GLCM Techniques

Skin is the primary vital largest organ of Human Body. It is seen than the majority of the people in the world doesn’t experience a good skin life due to various factors and causes. Dermatology is expected to have a startling global market of 8.7 billion dollars in 2022 as it grows daily. It is anticipated to reach approximately $30 billion in value between 2023 and 2028. Skin-related problems are to be expected following COVID-19 and the periodic changes in our lifestyle. In general, genetic or hereditary factors can be used to explain this problem. However, lifestyle factors may also play a role. Skin conditions can affect newborns, young children, teenagers, adults, the elderly, and others. In-depth analysis of the underlying cause and solutions is provided in "Artificial Intelligence in the field of Dermatology. " Prediction, diagnosis, and treatment are made more effective when a wide range of solid information is readily available. Artificial Intelligence and technology find a great place in the field of dermatology to comprehend, forecast, and create a wonderful future. Lack of additional data or information for machine learning processing is one of the problems. Despite the field's depth, there is room for improvement in terms of using technology to benefit patients. These are the main elements of challenges; the topic of this essay is skin dryness, which is a prevalent issue. Eczema, dermatitis, skin tumors, and itchiness are all associated with a higher likelihood of dry skin. The suggested method uses Second Order GLCM techniques to identify and examine the skin's textures in dermoscopic images. The improved performance of the classification model will be aided by the feature metrics that were obtained.
Vision Transformer and Attention-Based Melanoma Disease Classification

This study delves into the critical domain of melanoma detection, a life-saving endeavor that hinges on early diagnosis. Melanoma, a deadly form of skin cancer, poses a formidable challenge due to its tendency to remain dormant until advanced stages. Dermoscopic images serve as valuable tools, but distinguishing melanoma from non-melanoma lesions is notoriously complex. This paper explores the potential of Vision Transformers (ViTs), a novel deep learning architecture equipped with self-attention mechanisms, to enhance melanoma classification. We investigate how ViT's attention mechanisms can capture intricate features in dermoscopic images. The study also delves into fine-tuning strategies specific to medical image analysis. Through rigorous experimentation, our ViT-based sys-tem demonstrates promising results and training accuracy of 97% and testing accuracy of 91% highlighting its potential to revolutionize melanoma diagnosis.
Research on Chinese Semantic Similarity Algorithm

The rapid development of information technology and the rapid popularization of mobile terminals have promoted the transmission of information, and the growing amount of text data has become an important source of information for people to understand. This article studies the use of Word2vec to calculate word vectors to improve semantic similarity between texts, overcoming the shortcomings of traditional methods that rely on background knowledge bases, dictionaries, etc. to calculate similarity between texts. The accuracy of similarity calculation is compared with traditional cosine similarity and editing distance similarity. The implementation shows that the similarity calculation method based on Word2vec improvement proposed in this article has higher computational accuracy.
Object Detection and Recognition in Dark Using Feature Enhancement Algorithm

Computer vision makes extensive use of object recognition, which is essential for many applications. Object recognition approaches have now evolved into his two major various computer vision techniques. Object detection methods are described in this article. First, get an introduction and overview of current techniques in machine learning. Categories of classical machine learning using deep learning techniques. In everyday life, it is usual to use video cameras to monitor the campus. The majority of these surveillance methods employ people to keep an eye on what is happening in the target region. Yet, employing humans for monitoring has drawbacks of its own. To get around this restriction, researchers are developing automated visual surveillance systems. Environment modelling, motion segmentation, object categorization, tracking, behavior comprehension, person identification, and data fusion are the phases that make up the visual surveillance process. Finding moving objects in a video sequence is the first and most important step in visual surveillance. A person, a car, or another item may be the moving object of interest. The technology known as object detection is concerned with determining the semantic class of the moving item in the video sequence. Hence, Object Detection is crucial for following moving objects and analyzing their behavior in the provided video sequence. This study discusses the many object detection techniques that are available, taking into account the significance of object detection in visual surveillance.
A low-cost three-axis force sensing system to estimate interaction forces in robotics tasks

This work presents a force sensing system design based on strain gauges to measure interacting forces between this system and the body of a humanoid robot, developed in the Autonomous Robots and Cognitive Systems Laboratory (ARCOSLab) as well as between this system and large and heavy objects that are hardly manipulated in general by the hands of any humanoid robot. The experimental results proved the effectiveness of the force sensing system to measure contact forces in z, y and x axes with low cross-talk in certain ranges, and this allows to have suitable force reference values to validate a further system that will be integrated in the humanoid robot to estimate external contact forces.
Deep Learning-Based Waste Classification: Enhancing Recycling Efficiency and Sustainability

All countries should make recycling a priority. To recycle efficiently and save money, there is a need to classify trash into different categories. In this study, the focus is on the problem of locating a single garbage item in an image and assigning it to one of several possible recycling bins. Several ways have been studied to devise a complete solution. The models studied are a convolutional neural network (CNN) with Random Forest, kNN, naive Bayes, and a support vector machine (SVM). This research presents a new waste classification approach that uses deep learning technology to determine the type of trash automatically. It helps in reducing waste and improves the efficiency. The experimental results show that simple CNN and ANN networks produce promising results. Using deep learning techniques, the garbage categorization problem has been resolved with an accuracy of 97%.
Efficient Graph Formulation and Latent Space Integration for Lunar Hyperspectral Image Classification

This paper presents pioneering research that compares different graph formulation techniques for mapping minerals on the lunar surface from hyperspectral images (HSIs) obtained from the Imaging Infrared Spectrometer (IIRS) sensor on-board the Indian Space Research Organisation's (ISRO) Chandrayaan-2 mission. The main focus of the work is on Graph Convolutional Network (GCN). Additionally, we explore the effectiveness of latent space projection for feature extraction and generalization purposes. By leveraging efficient graph formulation techniques and incorporating latent space projection, we aim to improve the interpretability and performance of the GCN in the context of lunar surface mineral mapping. Comparative analyses including fixed adjacency matrix and spectral clustering are used to identify the most efficient approach.
Named Entity Recognition Method for Sports Events based on Boundary Span Information

Named Entity Recognition (NER) is a research hotspot in the field of NLP, which is often used to identify entity boundaries and categories in text. In the field of sports events, due to the high complexity of the event name, the traditional named entity recognition method is difficult to accurately identify, resulting in low recognition accuracy. To address this problem, this paper proposes a named entity recognition method for sports events that integrates boundary span information. It mainly uses regression operations to refine the spatial location information of entities in sports field texts to improve the precision of recognition. This method predicts entity boundary words by adding boundary detection to improve the ability to capture boundary span information, and then realizes high-precision recognition of named entities of sports events. The experimental verification is carried out in the large-scale real corpus SGSum. The method achieves 97.91%, 98.22% and 97.39% F1 values in the identification of event name, team name and competition location name.
Concurrent Vibration and Location Detection Using W-band On-chip Super-Regenerative Oscillator-Based Pulsed Radar

This work presents a W-band super-regenerative-oscillator (SRO) based pulsed radar sensor integrated with an SIW monopole antenna, enabling simultaneous detection of target vibrations and locations. The radar operates in the logarithmic mode with a 20 kHz sinusoidal wave injected as a quench signal. The distance can be obtained by calculating the time of flight between the transmitted and received signals through the cross-correlation method in the time domain. The experimental results confirm that the sensor can accurately identify the position of the target, which is a metal plate installed on an actuator. Further-more, the Doppler signatures can also be detected simultaneously using the proposed SRO-based on-chip pulsed radar, where the measured vibration frequency of the actuator agrees well with the ground truth.
IoT-Enabled Energy Consumption Monitoring and Control System for a Single-Phase Building

The increase in energy consumption demand has prompted the development of an IoT-based energy consumption monitoring and control system for single-phase buildings. This study aimed to integrate IoT-based monitoring with energy control using a PZEM004t module, ESP32 TTGO TCALL, relays, and contactors directly installed in the breaker. The objectives were to design and develop an energy control mechanism with IoT-based monitoring and to validate its accuracy and precision. The methodology included system design, hardware and software testing, and performance evaluation. The study found that the proposed IoT-based energy consumption monitoring and control system provided precise and accurate data on energy consumption, as evidenced by an average percent error of only 0.5 when compared to a watt-hour meter. The mobile and web application integration made it easy to remotely monitor and control energy consumption. Experimental data validated the system’s accuracy and precision, demonstrating efficient energy consumption control. In conclusion, the study demonstrated the feasibility of using an IoT-based energy consumption monitoring and control system to regulate energy consumption in single-phase buildings. The system’s integration with mobile and web applications makes it a viable solution for sustainable energy management in modern buildings.
RF Energy Harvesting Rectenna Using Triple Slotted Elliptical Patch Antenna And Small Rectifier: Empowering IoT Applications

The pursuit of sustainable and self-powered Internet of Things (IoT) devices has stimulated significant interest in innovative energy harvesting techniques. This research presents a novel approach employing a radio frequency energy harvesting (RFEH) antenna based on a triple slotted elliptical microstrip patch antenna (EMSA) design, supplemented by an efficient rectifier circuit. Advanced electromagnetic simulation tools, namely HFSS and ADS software, were utilized for antenna design and optimization. Operating within the Wi-Fi frequency range (5 GHz to 5.8 GHz), the proposed antenna demonstrates excellent performance characteristics. Simulated results exhibit desirable attributes, including low return loss, a VSWR below 2, a gain of approximately 2 dBi, and indicative radiation patterns and current distributions. Furthermore, the transfer of power to the rectifier circuit is significantly improved by an impedance matching circuit that uses microstrip transmission lines. Within the system, a voltage doubler rectifier circuit skillfully transforms the gathered RF energy into direct current (DC) voltage. The output power achieved reaches approximately 1.5 V at 5 dBm input power, showcasing the viability and potential of this solution for powering IoT applications. Therefore, it offers a viable solution to the problems with energy efficiency that IoT devices confront.
Design and Performance Comparison of Latency Efficient Scheduling Algorithms for Smart Environment

Smart Environment aims to improve humans' quality of life. IoT is an emerging paradigm that makes the environment smart by using sensing technologies in IoT devices and cloud computing technology. Various surveys portray that IoT devices are growing rapidly which results in more data which leads to more delay in response time for the services provided in the smart environment since the Cloud server imposes more transmission latency. This transmission latency is reduced by the emergence of Edge computing in which the services can be provided by using Edge as a computing device. However, the Edge is a resource-constrained device, so the proposed work aims to identify the process that can be processed at the Edge based on context and priority based scheduling. Therefore, an algorithm is designed to execute the process that needs immediate attention by applying context and priority-based scheduling. In a comparative analysis of algorithms, the experimental results showcase that the proposed algorithms notably reduce latency in IoT processes.
Compact Multilayer AFSIW Diplexer

This paper introduces a compact diplexer designed in the emerging air-filled substrate integrated waveguide (AFSIW) technology. This key component found in RF front-ends takes advantage of the 2.5-dimensionality of the AFSIW technology to achieve a small footprint. First, an AFSIW multilayer E-plane bifurcation is studied in theory and simulation. Then, a compact multilayer diplexer demonstrator integrating two fourth-order pseudo-elliptic filters centered at 14.5 GHz and 15.6 GHz with 660 MHz and 630 MHz bandwidth (BW), respectively, is designed, manufactured, and measured. A fair agreement is obtained between simulated and measured results. The fabricated compact diplexer achieves 630 MHz (4.34 %) and 620 MHz (3.97 %) - 3dB BW with, respectively, 0.76 dB and 0.87 dB insertion losses (IL) at the center frequencies.
Interleaved Boost Converter (IBC) based fast EV charging system: Design and Simulation

Transportation system is considered to be one of the most important aspect for a developing nation irrespective of its mode (road, railway, air, and water). The development is more appreciated when it will be in collaboration with nature. Electrification in transportation system is a promising area where various problems of existing transportation system were addressed such as less noise, reduce the import of petroleum products and improve the quality of air. The main problem with electric transportation (mainly electric vehicles) is overnight charging. To meet this, different types of fast chargers were designed and developed in last couple of years based on fundamental converter topologies such as: phase shift full bridge (PSFB) converters, LLC resonant converter, dual active bridge (DAB) converter, CLLC converters, and DC-DC boost converters. However, various limitations of the existing converters such as: high switching losses, large inductor size and high electromagnetic interference have motivated researchers to develop novel converter topology. The interleaved boost converter (IBC) based power electronic circuits are finding huge application in fast charging of EVs because of many advantages over fundamental boost converters in terms of low ripples in input current and output voltage, high efficiency, and low switching losses. In this research article, an IBC based 26kW fast charging system for EV is designed and simulated in MATLAB environment. Mathematical analysis of IBC in various modes of operation along with design parameters are well explained. A Nissan leaf 2013 model battery pack specifications where considered for simulation. Further, proportional integral (PI) controllers are applied to control EV battery parameters in constant current (CC) mode and constant voltage (CV) mode depending upon the state of charge (SOC) of EV battery.
Wireless Virtual-Reality by considering Hybrid Beamforming in IEEE802.11ay standard

In this paper, the problem of resource management in wireless virtual reality (VR) is studied. Rate of information transmission in VR is essential in acquiring QoS so for ascertaining low latency in VR application, mmWave technology should be used. In wireless LAN technology (WLAN) this frequency band is introduced in IEEE802.11ad/ay. Resource allocation in this standard is MU-MIMO with OFDM modulation. Working at mmWave frequency band requires massive number of antenna so for reducing the complexity in hardware it is better to select some number of them by hybrid beamforming. Processing delay, transmission delay and queue delay should be considered in acquiring QoS metric. Our metric is based on these delays that are computed by downlink and uplink rates from hybrid beamforming. Trend of transmission delay and our utility function in various SNR values are analyzed.
Robust PETS reinforcement learning algorithm for uncertain environment

Reinforcement learning (RL) is a branch of machine learning that learns optimal strategies through the interaction of an intelligent body with its environment. The real and experimental environments are different, which is the problem of environmental parameter uncertainty, which puts high demands on the robustness of RL. In this paper, we propose a robust reinforcement learning algorithm RPETS based on PETS for continuous state and action space. The algorithm makes collective decisions by training multiple PETS models to reduce the sensitivity of policies to model perturbations. In GYM customized environment, compared with classical PPO, TRPO, DDPG, SAC algorithms, this algorithm has better return mean and variance than other algorithms, and for the number of environment change parameters 1, 2, 3, this algorithm outperforms classical algorithms by 78%, 42%, and 23%, which proves that this algorithm has good robustness.
A Ku-band High Gain 40 W GaN HPA MMIC for Satellite Systems in a 0.25- μm GaN Technology

This study focuses on the design and validation of an internally matched high-power amplifier monolithic microwave integrated circuit (MMIC) for Ku-band solid-state power amplifier (SSPA) system integration. The designed MMIC exhibits sufficient power gain and output power characteristics, making it suitable for SSPA applications. The MMIC offers a compact and cost-effective SSPA configuration by reducing the number of components, such as gain blocks, isolators, and power combiners. The results show a performance of more than 40 W output power with a considerably high saturated power gain (greater than 22 dB) over the entire band (15.25–16.25 GHz).