Miracle-dz/UniFAR-FTUs huggingface dataset:
An Improved Semantic Segmentation Network for Ultra-High Resolution Remote Sensing Images Based on DeepLabV3+

With the continuous development of computer vision, semantic segmentation of remote sensing images has been known as a research hotspot in the field of remote sensing. In this paper, we propose an encoder-decoder structure to improve the DeepLabV3+ network based on ResNet for semantic segmentation of ultra-high resolution remote sensing images. We use ResNet18, ResNet101, and ResNet152 as the backbone of the network, respectively, and combine atrous spatial pyramid pooling (ASPP) to extract the semantic information of the remote sensing images at multiple scales and then reduce the feature maps to the input size by bilinear interpolation to realize the "end-to-end" pixel-level image classification and precise localization. We conducted experiments on the ultra-high resolution remote sensing image dataset ISRPS Vaihingen to verify the effectiveness of the proposed network. The experimental results show that the improved DeepLabV3+ network outperforms the original DeepLabV3+ network by 5.05%, 9.24%, 9.05%, and 7.62% in PA, MPA, MIoU, and FWIoU, respectively.
Combined RF-Ultrasonic Wireless Powering System for Sensor Applications in Harsh Environment

Sensors are vital tools in monitoring operating conditions and collecting data in scientific studies and engineering works. However powering sensors for a long term in harsh environments where physical access is difficult like enclosed oil pipes, sealed containers and underwater isn’t straightforward. To solve this problem, a novel hybrid power supply technique is proposed, combining the advantages of radio frequency and ultrasonic power transmission. By harnessing the penetration capabilities of ultrasound and the long-distance wireless transmission benefits of electromagnetic waves, the proposed a combined RF and ultrasonic wireless powering (CRUWP) system to power sensors deployed in harsh environments. This article builds a system model and introduces 90% efficient time-sharing energy management scheme for coupled circuits to address the power density mismatch between RF and ultrasonic energy transmission. Furthermore, the power budget analysis is presented for the hybrid power supply scheme, showcasing its capability to deliver 1 mW output power to the load.
Comparative Analysis of Ring Oscillators Based on Different Feedback Paths

By analyzing the interpolation linearity and accuracy of the output phase and phase interpolation clock data recovery circuit of the VCO, this paper aims to design a ring oscillator that can provide 12 Phase clock signal for Phase Select/ phase Interpolator-Clock and Data Recovery circuit (PS/PI-CDR), the frequency range of 50MHz ~ 4GHz. The parameters of two different feedback paths were compared and analyzed at 28nm process, and the phase noise of the two structures was analyzed using the pulse sensitivity function of the Hajimiri model, and a better topology was finally selected. In addition, in the delay unit, the starting vibration auxiliary device is added, which can make the oscillator be used with the start, and greatly reduce the starting vibration time. The simulation results show that the frequency adjustment range of the feedback ring oscillator is 0.05GHz ~4.29GHz, and the phase noise is -82.97dBc/GHz(@1MHz) when the frequency is 2.5GHz. At the same frequency and power budget, it is about 3dB lower than the feedforward ring oscillator.
An Efficient Approach for Detecting Neurological Tumors using Deep Learning

Tumor is a distorted bundle of tissue cells in which cells multiply endlessly, with no control over cell proliferation. Patients with early disease detection and treatment have a higher life quality and a longer expectancy of life. MRI imaging is useful in brain tumor investigation, diagnosis, and therapy planning. It assists doctors in determining the previous stages of a brain tumor. Detecting brain cancers with MRI scans is difficult due to the complicated structure of the brain. The study in this research article tackles the essential healthcare dilemma of effectively and timely diagnosing Brain Tumors. Deep Learning study used for classifying glioma tumor(glio_tu), meningioma tumor(menin_tu), no tumor(no_tu) state of the brain and pituitary tumor(pitu_tu). The research entails meticulous data preprocessing, proprietary CNN model architecture adaptation, and fine-tuning a collection of medical images. This study aims to enhance model performance through extensive validation and hyperparameter adjustment. Preliminary experimental outcomes demonstrate having custom Convolutional-Neural-Network (custom-CNN) model is very much accurately differentiate between distinct brain tumor states, with a 0.9850 validation and loss of 0.05. According to the research technique used, the combination of recent advances in deep learning combined with image processing and predictive modelling presents an intriguing way to identify solutions. These unique methods can then be used to assess accuracy and precision in the domain of brain tumors and other domains.
Target Detection in Complex Environments using UAV and Deep Learning-based Focal Pruning Method

Applications from computer vision that use deep learning for target detection have become incredibly popular recently. The requirement for future applications includes monitoring, visual navigation, object identification, and sensor-based preventative planning, together with the growing UAV industry advances, which hold great potential for deep learning. Deep learning techniques for object detection have swiftly evolved into a rendering tool for photos taken by flying drones. This paper's primary goal is to present a comprehensive overview of contemporary deep learning-based object recognition algorithms and to explore their most recent contributions to UAV low-altitude datasets. As opposed to standard or remote-sensor datasets, which comprise a smaller percentage of the literature, low-level UAV datasets are the focus of the research. To conduct real-time bridge crack diagnosis with Unmanned Aerial Vehicles (UAV), a deep learning system dubbed the You Only Look Once version-4 Focal Pruning Method (YOLOv4-FPM) is suggested based on the YOLOv4 model. For YOLOv4-FPM, focal loss architecture is utilized to streamline the loss feature, improve accuracy and colour transmission, and solve issues with dynamic context. A pruning method is used to speed up detection and facilitate the network. The outcomes show that the model can successfully handle the difficulty given by various backdrops, and the detection velocity is 97.2% effective enough for the real-time detection mission. The YOLOv4-FPM of deep learning technology guarantees 96.3% detection accuracy compared to current drone detection methods.
The Effect of Incentive Mechanism on Knowledge Transfer Network: An Agent-Based Model Approach

Knowledge transfer plays a vital role in the growth and sustainability of organizations. It also creates opportunities for employees to work together and share ideas which are helpful for their professional development. Organizations can keep innovating, improving, and competing in a rapidly changing environment if they maintain knowledge properly. Unfortunately, rational individuals tend only to participate in knowledge transfer if the costs needed is well compensated both internally or externally. Incentive is one of the external drives that companies usually apply to stimulate knowledge transfer. This research aims to analyze the effects of incentive mechanisms on knowledge transfer activities using agent-based model approach. Based on the interaction among agents, we construct a knowledge transfer network. The result suggests that incentive mechanism positively correlated with knowledge transfer efficiency. However, the effect is not significant to change the network structure.
A Multistage Malware Detection and Classification Model Using Visual Features

The development of the Internet and its widespread usage among users has led to an increase in malware activity in recent years. It has become increasingly challenging to identify and categorize malware samples using current anti-malware scanners as they have become capable of evading detection through obfuscation. A simple yet effective multi-stage model based on CNN is proposed for viewing, detecting, and classifying malware. The dataset is generated by combining Microsoft's Malware Classification Challenge dataset and a compilation of benign open-source code files. A visualization technique is proposed to convert different code files to images and the result exhibits an accuracy of 95% for detection and 94% for malware classification.
Model Adjusted Generalized Tests for Methane Plume Detection on Hyperspectral Images

Reducing methane emissions is essential to tackle climate change. Here, we address the problem of detecting automatically point source methane leaks using high resolution hyperspectral images from the PRISMA satellite. We test the Generalized Likelihood Ratio Test (GLRT) for plume detection and compare it to the Matched Filter (MF). We then propose an improvement of the GLRT by using an adjustment coefficient. We introduce this new method under the name: Model Adjusted GLRT (MA-GLRT). We show that the MA-GLRT method reduces the fraction of false detections compared to the MF and the standard GLRT without preventing the detection of plumes. To validate the method, we use a dataset of manually annotated plumes on PRISMA images. We then show that our method outperforms the matched filter and the GLRT in terms of F1 score.
KIKO: A Tools Framework for Industrial Domain-specific Natural Language Processing Tasks

Domain-specific Natural Language Processing (NLP) tasks need prepared, ground-truth data. However, practical constraints make this task challenging. Domain engineers, who are the subject matter experts, are required to create high-quality training data. However, their availability is limited because they are busy with their day-to-day high-priority project and organizational tasks. Moreover, the data needed to be annotated to train NLP models may not be available at the outset itself. As the prepared data grows, NLP models should take advantage of the new data and train improved machine learning models. Hence, in this paper, we present KIKO, a domain-specific continuous learning NLP framework that allows domain engineers to perform annotations on one document at a time, and retraining when a large enough collection of documents becomes available. KIKO is made of several tools that pipeline together to process documents from the industrial domain. The pipeline enables parsing, pattern-matching, annotation, training, relationship mining, and continuous learning based retraining of the model by using a weaker form of active learning.
An MPC-Based Oscillation Suppression Strategy for IBRs connected to weak grids through LCL filters

This paper presents a model predictive control (MPC) based oscillation suppression strategy for LCL-type grid-connected inverters in weak grids. A hybrid cost function is developed to efficiently implement virtual damping and seamless transfer schemes. The method elaborates on different control modes for normal and under-transition operations. The LCL filter's voltage across the grid side inductor is utilised to derive the reference current to minimise transient oscillation and limit current spikes. The developed scheme will switch the MPC's reference current and cost function to manage different oscillations for inverter-based interconnections. Simulation results will prove the feasibility of the proposed strategy.
Exploring Pixel Segmentation with Mask R-CNN: Implications for Predicting Cattle Weight

In the realm of livestock management, particularly concerning cattle, the swift and precise estimation of animal weight is of paramount significance. This research delves into the intricacies of image data preprocessing utilizing various combinations of the Mask R-CNN approach to support deep learning-driven cattle weight prediction models. The renowned efficacy of Mask R-CNN in object detection and segmentation is harnessed to emphasize features specific to cattle. Rigorous experiments were undertaken, encompassing 31 distinct datasets, aggregating a total of 223 images of Bali cattle before augmentation. The empirical findings emphasize the superiority of the Mask R-CNN R50-DC5 and R50-FPN backbone combinations, which exhibited commendable precision rates of 86,1% for R50-DC5. 85,8% for R50-FPN, and 85,1% for R50-C4. Additionally, observations were conducted to ascertain the most efficient process speed among these configurations. The manuscript aims to select the optimal image segmentation method to acquire pixels that will be explored as features in predicting the weight of livestock. The results obtained underscore the importance of ensuring meticulous evaluations of image segmentation accuracy to maintain the integrity and reliability of the model.
Loosely Coupled Localization System Based on Multi-Base Station UWB and Point-Line VIO

Accurate state estimation during the long-term operation of a monocular visual-inertial navigation system (VINS) in complex environments is a challenging task, to achieve globally accurate and drift-free localization, we employ a Multi-Base Station Ultra-Wideband (UWB) positioning system to provide global constraints for the local visual-inertial navigation system. To address the need for precise localization in low-texture and low-light environments, we propose a loosely coupled localization system that combines Multi-Base Station UWB and Point-Line Visual-Inertial Odometry (VIO). This system integrates the global Multi-Base Station UWB positioning system with the local Visual-Inertial Navigation System. Firstly, the front-end VIO fuses line and point features to extract structured feature information in low-texture environments, enabling more accurate local poses in complex environments. Secondly, the Multi-Base Station UWB positioning system provides global poses in the localization scenario. Finally, local VIO poses and global UWB position information are fed into the loosely coupled framework, facilitating global nonlinear optimization to obtain accurate global pose. We compare our system with existing frameworks on challenging public datasets, and in the same environments, our system exhibits lower localization errors.
Spectral Unmixing in Generative Space: 3D-GAN Based Approach

Spectral unmixing in planetary data encounters challenges like noise, limited samples, and high dimensionality, affecting its efficacy. Deep learning methods, while highly performant, often lack interpretability and generalizability. In this study, we comprehensively evaluate spectral unmixing techniques, considering accuracy, efficiency, interpretability, generalizability, noise robustness, and sample requirements. Our primary focus is on Generative Adversarial Networks (GANs), specifically comparing the nonlinear Polynomial Post-Nonlinear Mixing Model (PPNM) with our proposed 1D-GAN and 3D-GAN. In our GAN approach, one CNN discriminates inputs, and another generates fake inputs. They train adversarially, enhancing discriminative CNN’s generalization, crucial with limited samples. We propose a 1D-GAN for spectral classification and a robust 3D-GAN for spectral-spatial tasks. Using generated adversarial samples for fine-tuning, we improve endmember abundance accuracy. We apply this method to hyperspectral image (HSI) obtained from the Imaging Infrared Spectrometer (IIRS) sensor onboard the Indian Space Research Organisation’s (ISRO) Chandrayaan-2 mission yielding competitive results. Our GAN approach opens new opportunities in remote sensing for challenging spectral unmixing, revealing GAN potential in analyzing complex, nonlinear data.
Patch-Based Multi-Level Attention Mechanism for Few-Shot Multi-Label Medical Image Classification

Few-shot learning stands as a prominent trend in the field of computer vision, with substantial applications in vision tasks such as image classification and semantic segmentation. It has gained popularity due to its potential to reduce the demand for computer resources and its ability to lessen dependence on large datasets. However, generating high-performance models becomes challenging since this approach must generalize only from a limited set of samples. This challenge is particularly evident in multi-label medical image classification, where overlapping labels and obscure characteristics within specific image regions impede the generalization capabilities of few-shot learning. This paper proposes a patch-based strategy with a multi-level attention mechanism. Our approach employs patch-based methods with multi-level attention to segment regions with overlapping information in images, thereby facilitating the extraction of crucial feature data. Experimental results reveal that the patch-based technique can help multiple models achieve greater classification performance across various datasets, demonstrating that the strategy effectively addresses the challenges inherent in multi-label classification.
Prediction of Age and Gender from Masked Face Images using Convolution Neural Network

The COVID-19 pandemic has severely disturbed our regular lifestyle. It is now an accepted practice to cover one’s face to protect the health of oneself and others. In this current situation, to ensure both their own and their clients’ safety, several public service providers demand that their clients properly wear their face masks. Many private and government organisations have also mandated face masks so that the safety of any of their employees is not compromised and a safe working environment is created for all. Even after making face masks mandatory in public places and in private and government organisations, some people still neglect to wear them. Moreover, those who do not wear face masks correctly may render them ineffective. This kind of problem makes faces mask detection algorithms a critical tool to get the solution in this domain. In this paper, we used the concept of convolution networks to develop the proposed model and trained the model on the dataset on numerous epochs until we obtained a higher accuracy than the compared state-of-the-art models. The accuracy achieved with our model on the selected dataset was near 94% whereas the state-of-the-art models got near 89% accuracy on the same dataset.
A cooperative positioning algorithm based on adaptive selection and factor graph

The cooperative positioning technology based on Autonomous Underwater Vehicles (AUVs) is one of the most rapidly developing underwater positioning technologies in recent years, it can complete complex underwater tasks that are difficult for a single AUV to complete, and has broad development prospects. Due to the complex underwater environment, fuzzy time-varying noise may be generated, which may also lead to changes in the system structure, the number of leader AUVs is numerous and the quality is complex. These factors can lead to a decrease in the accuracy and efficiency of traditional algorithms. To address these issues, we propose a cooperative positioning algorithm based on dynamic structure-based adaptive selection and Factor Graph (DS-AOSFG). Systems with dynamic topology can effectively cope with structural changes. In response to fuzzy time-varying noise, we introduce an adaptive filter module. In response to the problem of many leader AUVs and mixed quality, we introduce a node optimization selection module and improve the traditional algorithm through the factor graph algorithm. The final evaluation indicates that our DS-AOSFG method outperforms other algorithms in terms of positioning accuracy and speed.
Combining Strategy and Roles to Formally Analyze Multi-Agent Reinforcement Learning

A novel semantics for MARL called neural concurrent game structure (NCGS) is introduced, which extends CGS with neural network and roles where the agents are implemented via feed-forward ReLU neural networks. To formally verify concrete NCGS systems and reduce the complexity, multi-role strategy logic(mrSL) was put forward. mrSL is suitable for the properties of the NCGS, regardless of which agent is responsible for the specific task. Parameterized model checking(PMC) is used to solve the NCGS verification problem against mrSL. Finally an algorithm for MILP-based verification process is introduced, and report the experimental results.
MFCC-Based Analysis of Vibratory Anomalies in Parkinson's Disease Detection using Sustained Vowels

Parkinson's disease (PD) is a neurological disorder resulting from the degeneration of dopaminergic neurons, which leads to speech production disorders, particularly at the level of the vibratory and articulatory aspects. Early clinical observation of voice disorders can be difficult, hence the growing interest in voice analysis-based diagnoses using machine learning methods. Mel frequency cepstral coefficients (MFCC) are widely used in speech and audio processing as they capture valuable insight about the shape of the vocal tract. The objective of this study is to investigate the relevance of MFCC coefficients as descriptors to identify abnormal vibratory patterns indicative of Parkinson's disease during sustained vowel production. To do this, we evaluate the performance of MFCC in discriminating voices of patients with PD from healthy voices, without combining them with any other speech features. We also perform a comparative study of different classification techniques, such as KNN, SVM, DT, and RF.
An Intelligent Design of Solar Energy Powered Smart Residence Controlling System Using Novel Power Grid Principles

The potential for harnessing solar energy to produce power for daily life is great. According to scientific estimates, the sun can generate enough energy to run the whole planet’s electrical grid for an entire year in just an hour. Solar power systems constitute one of the most effective and long-lasting renewable energy choices currently on the market. The ambition of the Internet of Things (IoT) is that it will one day encompass all physical items in existence. The IoT enables the remote sensing and/or controlling of objects over preexisting network infrastructure, opening up new possibilities for the seamless blending of the real world and digitally implemented systems. This kind of technology has multiple uses like Renewable towns and cities, Effective communities, micro-grids and PV-Street lamps and so on. As During this time period, the use of renewable energy sources expanded at a rate never before seen in human history. Solar power use may be displayed in real time online using the suggested approach. Arduino is used for this kind of monitoring. Intelligent Tracking reveals daily renewable energy consumption. As a result, the user is able to manage the on/off state of connected devices using an IoT-based application. Off-grid or freestanding systems can be characterized as separate structures that have no connection to any electromagnetic grid. These may be found in a variety of sizes and are typically put to use in areas with limited access to grid-based utilities. This study investigates whether or not adopting an off-grid/standalone system strategy may help us become energy independent and reduce our dependency on the grid.