Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-class Fault Analysis Using Quadratic SVM Classifier Fault Detection Technique for Micro-grid

An innovative and intelligent method is used to identify and diagnose micro grid electrical faults in this paper. In a dynamic distribution network with integrated distribution generation (DG), or micro-grid, fault detection is crucial. Intelligent approaches can help the micro-grid discover issues independently. In our case, the microgrids employs batteries and solar panels in which faults in the grid integration of a solar and battery energy storage system (BESS) must be detected and established quickly. Due to the complexity of linking two-generation systems with the grid, which involves many parameters, the Simulink model simulates micro-grid normal operation and failure scenarios. The simulated fault circumstances depict distribution line network faults. To reach our objective of the study the Quadratic Support Vector classifiers will be compared to linear regression, Kernal Naives bayes, and sub-space discriminant intelligent detection methods. Training and testing these models will use feature-extracted voltage and current samples. The classification rate, expressed in statistical metrics, shows that quadratic support vector is more efficient and accurate than other classifiers which it tested and trained against, with the next best classifier 3.3% below it in accuracy. We substantiate our claims graphically with scatterplots and confusion matrices for each classifier.
Cluster-Specific Dictionary Learning Based Active User Detection for mMTC With Massive MIMO

Massive machine-type communication (mMTC) is an important scenario for 5G and future 6G networks, as it can provide massive connectivity for internet of things (IoT) devices. However, the large number of supported devices raises challenges to random access with limited spectrum resources. In this paper, we propose a dictionary learning based method for active user detection (AUD) in massive MIMO systems, which leverages the potential spatial channel characteristics of users. Our approach separates users into clusters and reuses the same pilot pool among different clusters, which greatly saves the pilot resource. To resolve collisions caused by the reuse of pilots, we propose a cluster-specific dictionary to differentiate multiple active users of different clusters. Numerical experiments demonstrate the improved performance of the proposed AUD algorithm in comparison to the existing methods.
Efficiency and Precision: Control Systems Empowered by Smart Image Processing

The rapid increase in the population has resulted in traffic jams on the roads. There was a corresponding increase in the number of calls for emergency services, such as ambulances, police cars, and fire trucks. Maintaining rapid response times for emergency vehicles is critical right now. In this study, we present a smart traffic control system that integrates Internet of Things (IoT) chips into traffic signals and modifies their timing parameters. These chips will function in response to a road's estimated automobile count and the subsequent need for emergency vehicles along that route. The number of people in each automobile is going to be calculated using digital image analysis of the live camera feed. To make sure emergency vehicles get the help they require, GPS systems are going to be utilized to track the vehicles and update the chips with their current locations. It is envisioned that traffic lights on a road network will function by dynamically assigning weights for a particular node or the time among two consecutive lights. In order to maintain track of each node in the network, the weights assigned to each one must be updated in real-time.
A Fully solution-casted low-voltage driven organic TFT by Floating Film Transfer deposited organic channel on Lanthanum Oxide dielectric film

In this work, a self-oriented, fully sol-gel casted cost-effective organic TFT has been fabricated with pristine P3HT polymer as an organic semiconductor channel on high-k Lanthanum Oxide as a gate dielectric. The organic semiconductor channel has been deposited by utilizing a unique floating film transfer (FTM) technique, which has the advantage of minimal wastage, a simple self-oriented organic semiconductor film, enhancing film quality, crystalline properties, grain control, and further improving the carrier transfer phenomenon in the channel. The floating film transfer has the advantage of a simple process, no additional setups, and high-quality organic film over other solution-processed methods (Langmuir Blodgett (LB), Spin Coating, Drop casting, etc.) The fabricated dielectric film passes with a quite smooth surface roughness of 0.926 nm (AFM morphology) and has a high dielectric constant of ~20. Moreover, the fabricated lanthanum oxide film shows a high areal capacitance of 450 nF/cm2, a low leakage current density of ~10 nA/cm2, and a high band gap of 5.14 eV. The device passes with a drain-to-source current of 2.67 µA at– 4V VDS, VGS supply, a threshold potential of 0.47 V, and enhanced mobility of 0.08 cm2/V.s. The organic semiconductor film grown by the FTM method has enhanced crystallinity, oriented grain, and better surface morphology than other solution-processed deposition techniques. The study targets to develop a cost-efficient, simple, low-supply operated portable OTFT for various electronic and sensing applications in the near future.
FedChallenger: Challenge-Response-Based Defence for Federated Learning Against Byzantine Attacks

Federated Learning (FL) is an emerging paradigm that enables multiple clients to train a global model collaboratively without sharing their privacy-sensitive data. However, one of the significant challenges in FL is the aggregation of the model updates from different client devices, as malicious participants acting as Byzantine attackers can craft the model update and poison the global model. The state-of-the-art defence mechanisms mostly rely on aggregation-based security defences to improve the degraded accuracy. However, preventing attacker's participation in the training can have an impact on improving the global model's accuracy. Therefore, in this paper, FedChallenger, a dual-layer defence mechanism, is proposed, which attempts to detect and prevent malicious participation in the FL training process in its first layer. The other layer incorporates a trimmed-mean aggregation strategy, where pairwise cosine similarity identifies malicious updates and removes entire client updates from federated averaging. Extensive experiments using the BloodMNIST dataset validate that the FedChallenger gains nearly 85%, 80%, 15%, and 4% accuracy with more than 1.2 times faster convergence rate over the state-of-the-art Byzantine resilient aggregation strategies called FedAvg, Fang, Krum, and Trimmed-Mean approach, respectively, on 40% compromised devices. Above all, it shows consistently better results than them in both attack and non-attack scenarios.
Autonomous Motion Control of Underwater Manipulator Based on Dynamic System

Underwater operation environments are normally complex and dangerous. Therefore, highly intelligent underwater robots with adequate maneuverability are typically required to assist with operations in such environments. In this study, an autonomous motion control framework for an underwater manipulator was developed based on a dynamic system, providing a high degree of autonomy and intelligent underwater manipulator motion. The basic principle of the framework was expounded, and the solutions to two key problems of motion planning and obstacle avoidance were obtained. Further, the effectiveness of the control framework in autonomous obstacle avoidance and grasping and manipulation tasks was verified by the motion simulation of a manipulator platform. This research has certain guiding significance for the autonomous motion and obstacle avoidance control of underwater multifunctional manipulators.
Distributed DRL Based Beamforming Design for RIS-Assisted Multi-Cell Systems

Reconfigurable intelligent surface (RIS) has the potential to significantly enhance the performance of communication systems by dynamically adjusting wireless environment. In this paper, we aim to jointly optimize RIS matrices and beam-forming at base stations (BSs) in RIS-assisted multi-cell systems. Considering the high complexity of traditional algorithms, we adopt deep reinforcement learning algorithms (DRL) for the difficult joint design task. Moreover, to overcome the performance drawbacks of centralized DRL algorithms with large state-action spaces, we propose distributed DRL algorithms based on the distributed distributional deterministic policy gradient (D4PG) method and the federated learning (FL) framework. Experimental results demonstrate that distributed DRL algorithms can considerably improve learning efficiency while reducing complexity and communication overhead. Moreover, it can be applied to various systems with satisfactory performance and has better robustness compared to the centralized DRL algorithms.
Instantaneous Fundamental Frequency Estimation based on FBSE-FAWT

Instantaneous fundamental frequency (IFF) estimation plays an important role in speech processing. This paper proposes a method for estimating a speech signal’s IFF based on the Fourier-Bessel series expansion-based flexible analytic wavelet transform (FBSE-FAWT). The proposed method uses an FBSE- FAWT-based filter bank to decompose the signals into subbands. Components corresponding to the fundamental frequency (FF) can be present in more than one subband. Therefore, a set of subbands are selected whose mean frequency is found to be in the range of 50-500 Hz. In each subband, there may be components separated in the time domain. So for the separation of these components, a new method for time-domain segmentation (TDS) using the Teager-Kaiser energy operator (TKEO) is introduced. Then Hilbert transform separation algorithm (HTSA) based processing is performed on these separated components to obtain the IFF of the speech signal. As a post-processing step, cubic spline interpolation is performed to get accurate IFF. To validate the proposed method, the Keele database is used and the results are compared with the traditional methods. Experimentally it is proved that the IFF estimation method based on FBSE- FAWT is working better and improves the time-resolution of IFF estimation.
User-centered representation of data flows in mHealth applications

The rapid digitization of healthcare has led to a surge in the use of mobile health applications to manage and transmit personal health data. However, this development is not without risks, especially in terms of IT security and privacy. The use of tracking services without explicit user consent is a common problem with mobile health applications, as is the access to critical data by the applications. This paper presents a novel approach to categorize the data flows of mobile health applications according to their purpose and destination and to track the number of trackers and permissions used. The approach includes an evaluation of the applications according to derived data flow aspect categories and proposes a usability-tested, intuitive and user-friendly application to present these categories and ratings to users.
A Robust EfficientNet Architecture for Brain Tumor Classification and Identification Using MRI Image

The most common method for classifying brain tumors is by using biopsy, but this procedure is only carried out after surgery in which a tiny piece of brain tissue is takeout as well as analyzed under an electronic microscope to see a tumor. Since technology has advanced, particularly in ML and AI, we are now able to identify and categorize tumors without undergoing surgery by feeding the machine's classification engine the current data from various MRI images. By doing our study, we present a approach for the identification of tumors utilizing EfficientNet, an architecture that has already been trained utilizing the transfer learning methodology. This study emphasizes on the EfficientNet-B2, EfficientNet-B3, as well as EfficientNet-B4 algorithms from the group of EfficientNet approach. The suggested approach makes use of thresholds to enhance the dataset for more effective accuracy as well as data augmentation to improve the variety of brain images in the data in addition to the previously-trained approach to enhance the effectiveness of training an improved the model. Because it utilizes the compound coefficient to scale all three dimensions of an image with a fixed value of ratio, depth, width, and resolution—EfficientNet relatives of models outperform earlier CNN architecture, according to preliminary results. The outcomes also showed that scaling the previous approaches, enabled the model to capture complex features, improving the accuracy of the model.
Joint Design of Multi-Dimensional Multiple Access and Lightweight Continuous Authentication in Zero-Trust Environments

Continuous authentication is essential to realize the new zero-trust based security provisioning. Conventional authentication techniques often rely on security keys, credentials, or device fingerprints, which may suffer from either high network overhead or low reliability in highly dynamic environments. To concurrently overcome these challenges, we jointly design the multi-dimensional multiple access and lightweight continuous authentication (MDMA-LCA) to explore multiple domains of the users' access channels for both communication and security enhancement. The access time frame, subchannel, and power allocation of multiple users are formulated as a joint optimization problem to maximize the achievable sum rate (ASR) of the users while continuously authenticating their identities assisted by the non-orthogonal multiple access (NOMA). The proposed scheme achieves lightweight continuous authentication by prearranging the access time sequences of multiple users and by verifying them directly and simultaneously at the base station (BS). Then, the joint optimization problem is decomposed and transferred to a maximum flow problem in a designed graph, and a joint MDMA-LCA algorithm is developed. Simulation results demonstrate that, compared with several existing schemes, the proposed scheme achieves an ASR gain while guaranteeing the continuous authentication of the users.
Augmented EEG Signal Classification for Energy Data Visualizations

Classification of electroencephalogram (EEG) data is dependent on data size, quality, and generalizability to decode invaluable information from brain-computer interface (BCI) systems. EEG data collection typically occurs within controlled laboratory environments using long sections. Participation in these experiments draws less attention due to the challenging nature of the EEG experiments and the intended objective. Hence, an approach that offers the potential for both cost and time efficiency is the use of artificial data generation. Data augmentation can improve the classification performance by increasing training data. This study employs a Generative Adversarial Network (GAN) model to enhance the empirically collected EEG dataset, which was introduced for the purpose of analyzing the perceptions of energy users regarding energy data visualizations. The data size is initially expanded through the use of a GAN model. Subsequently, the augmented data is assessed in terms of its classification performance. Three different sample groups—defined as male, female, and mixed—are shown in the results. Each group is used to generate synthetic EEGs, separately. To evaluate the enhanced EEG data, a scenario of transforming EEG data into spectrogram images and then classifying them using a Convolutional Neural Network (CNN) is used. The results show that the female sample group performed the best in terms of valence and arousal, with accuracies of 87.9% and 89.06%, respectively. This study shows promising results in terms of increasing EEG data size and improving classification performance.
An Ultrasound-Based Surveillance System for Bathroom Posture and Location Estimation

Bathrooms can be slippery, increasing the risk of falling. In addition, because people enter the bathroom alone, it is difficult to detect accidents immediately when they occur. Therefore, a system is required to quickly detect accidents and call for assistance. In this study, the location and the posture of the bather are important attributes. We propose a method to estimate the location of a bather using ultrasound correlation values as input to a deep neural network and to estimate the bather’s posture. Therefore, the mean absolute error achieved 11.7 cm for location estimation, and accuracy achieved 95.3% for posture estimation in the experiment involving an individual moving throughout the bathroom.
Research on Sentiment Analysis of Travel Reviews Based on Word2Vec and RNN

Sentiment analysis of travel reviews is performed by using Word2Vec and RNN models to better understand and recommend the travel destinations. Firstly, Word2Vec is employed to train word vectors on travel reviews and convert them into vector representation, and an RNN model is employed for sentiment analysis. The experimental results are compared with SimpleRNN, CNN, and common machine learning-based models. The result shows that this method can effectively perform sentiment analysis on travel reviews and provide a valuable reference for travel recommendation systems.
Autonomous Diagnosis System of Breast Cancer

This paper focuses on a study of medical image techniques using deep learning, specifically addressing methods for diagnosing breast cancer. The research aims to enhance breast cancer classification and localization through image classification and segmentation techniques utilizing mammography, ultrasound, and histopathology images. Among various image classification and segmentation techniques, the study selects technology and loss functions optimized for medical imaging characteristics, along with proposing data augmentation methods. The research findings demonstrate that using filter-based techniques for data augmentation yields excellent performance in image classification using ResNet50. Additionally, for the segmentation of mammography and ultrasound images, the UNet architecture performs exceptionally well. Through the application of these techniques, the segmentation performance of mammography images improved by 33.3%, ultrasound image segmentation improved by 29.9%, and histopathology image classification accuracy increased by 22.8%. This research presents a contribution to deep learning-based medical image processing in the context of breast cancer diagnosis.
Research Progress on the Static Stability and Motion Stability for Cable-Driven Parallel Robots

Cable-driven parallel robot (CDPR for short), as one particular kind of parallel robots, is driving through flexible cables replacement of rigid links. It has been widely used due to its unique advantages at present. Cables can only be pulled and cannot be pushed, CDPRs are more prone to instability relative to rigid link parallel robots. Hence, for the CDPRs, stability is the key parameter of performance evaluation. And more theoretical researches on stability are needed to enhance the performance. Therefore, the theoretical researches on their stability is reviewed and summarized here. Their applications and research work in various fields are illustrated firstly. Then, the research progress of the static stability is analyzed in detail. Furthermore, the development of motion stability is summarized. The analysis results show that establishing a quantitative and reliable stability evaluating indicator for the CDPRs is necessary. This review will be helpful in the future studies for performance analysis of CDPRs.
Design and Analysis of Approximate Multipliers using Approximate 4:2 Compressors

Approximate computing techniques have emerged as a promising approach to enhance efficiency by sacrificing a bit of accuracy. Within this framework, approximate multipliers have gained significant attention for their ability to find a sweet spot between precision, performance, and power efficiency. One popular method for creating these multipliers involves using 4:2 approximate compressors, which act as efficient components in approximate arithmetic circuits. This paper introduces two different designs for 4:2 approximate compressors, which are then utilized to deliberately decrease the accuracy of the multiplier while achieving substantial gains in power efficiency and speed. The effectiveness of these proposed designs has been confirmed through simulations using the Genus software, demonstrating a noteworthy 30% and 34% reduction in the required area and a 42% and 44% reduction in power consumption.
Web App-based Multiple Diseases Detection Using Machine Learning: A Novel Approach

The increasing prevalence of various diseases calls for the development of efficient and accurate disease detection systems. In this study, we propose a systematic approach for multiple disease detection using machine-learning techniques using a web-based application. The proposed method utilizes a dataset of medical data and employs pre-processing, feature extraction, and classification techniques to detect and diagnose diseases. The system's performance was evaluated using various metrics, including accuracy, sensitivity, and specificity, which showed promising results. The proposed method offers an accessible and cost-effective solution for disease diagnosis, allowing for early detection and timely treatment. Furthermore, the developed web-based application can be deployed on a cloud platform, enabling easy access and scalability. This proposed approach can significantly improve disease detection and diagnosis, thus enhancing healthcare outcomes.
Chebyshev Individual Adaptive Exponential Functional Link Filter for Nonlinear System Identification

Recently introduced adaptive exponential functional link networks (AEFLNs) are widely employed nonlinear filters with linear-in-the-parameters. In addition, sinusoidal basis functions are used with individually varying adaptive exponential factors for enhanced nonlinear system modelling. To improve the performance of these nonlinear filters, a new Chebyshev individual adaptive exponential functional link network (CIAEFLN) is designed in this paper. An independently varying exponential term is included in the Chebyshev polynomial as a nonlinear expansion parameter to improve the accuracy and convergence rate. Bounds on the learning rate and weight update have also been derived for the introduced CIAEFLN technique. Simulation outputs demonstrate the improved performance of the proposed algorithm as compared to the present techniques for various nonlinear system identification.
Shallow machine learning fault diagnosis for industrial equipment based on vibration data

In order to improve the maintenance efficiency of industrial equipment and ensure its safe and stable operation, it is of great significance to carry out effective fault diagnosis of industrial equipment. The fault diagnosis by analyzing the vibration signal of industrial equipment has great feasibility and advantages. This paper firstly elucidates the significance of fault diagnosis based on vibration signals. Subsequently, it expounds on the current research status of shallow machine learning fault diagnosis in terms of signal noise reduction, feature extraction, and feature dimensionality reduction. The advantages and limitations of typical methods used in this technology are summarized, along with the identified issues that need to be addressed and future developmental directions.