Miracle-dz/UniFAR-FTUs huggingface dataset:
A Systematic Review of EEG Signals Classification Using Machine Learning and Deep Learning Approach

AI ML Based industrial scenario is called 21 Era for medical science, here, Human brain consists of millions of neurons can cause normal and abnormal activities. So, these abnormal activities can be cause of multiple disorders such as insomnia, paralysis etc. the human been are suffering due to abnormal activities which can be recorded using EEG signals (reports). These signals are non-stationary in nature and depend on the environment, life cycle, mental status, and other various causes of human life. EEG signals are helpful for decision-making in various applications, especially in the health sector. Seizures, a critical issue of the brain such as series arise from Epilepsy. Epilepsy is one of the most common diseases affecting the largest area of the population. By the application of EEG signal it is easy to diagnose this disease. But the Manual detection of disease is still a time-consuming process. This paper provides a comprehensive study on the performance based on various Deep learning methods and also provides an overview on machine learning methods like Support Vector Machine (SVM), K-Nearest Neighbor (KNN), linear discriminant analysis (LDA), decision tree (DT).
Boosting Oversampling Methods for Imbalanced Data Classification

Imbalanced data classification is a prevalent challenge in supervised machine learning, where minority class instances are often underrepresented in the data set. This paper explores the efficacy of AdaBoost, a renowned ensemble learning method, in conjunction with two oversampling techniques, Synthetic Minority Over-sampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN). In the proposed methodology, the data set is initially divided into training and testing sets. The training data is then divided into random subdata sets, each of which is used to develop unique models, which are commonly based on Decision Trees. These models are then weighted averaged together to generate the AdaBoost model. To address the issues of imbalanced data, misclassified instances from the testing set are extracted and combined to test set to produce a new data set. Both SMOTE and ADASYN oversampling techniques are applied to those misclassified data and their classification performance is compared using confusion matrix, accuracy, precision, and recall metrics. The purpose of this research is to provide insight into the suitability of AdaBoost in conjunction with oversampling strategies for dealing with imbalanced data classification. The experimental findings suggest that SMOTE is the best strategy for dealing with imbalanced data sets after oversampling rather than ADASYN.
Time Domain Phase Noise Mitigation in OFDM Systems for Sub-THz Bands

The phase noise (PN) generated by the oscillators limits the link performance of radio frequency transmission systems. In OFDM, the PN induces intercarrier interference (ICI) which needs to be addressed to limit spectral efficiency penalty. In this work, we propose a new algorithm called time domain filter (TDF). It consists in mitigating the ICI by estimating and compensating the PN in the time domain (i.e. before the Fourier transform operation at the receiver). It takes advantage of the correlated nature of the PN. We perform comparison between the proposed algorithm and those presented in the literature. We show through numerical results that the proposed algorithm outperforms the phase noise tracking algorithms in the literature, assuming 5G New Radio numerologies and simple pilot pattern,
IOT Based Soldier Tracking and Health Indication System

This paper presents the design of an IoT-based soldier tracking and health indication (STHI) system utilizing the Arduino Uno microcontroller integrated with vital sensing and communication modules. The system aims to monitor and provide real-time updates on a soldier’s location, health parameters, and physical condition, enabling timely intervention and support. The proposed system incorporates various sensors including SpO2sensor to measure blood oxygen saturation levels and heart rate, a temperature sensor to monitor body temperature, a vibration sensor to detect falls or physical impacts, a GPS module for precise location tracking, and a GSM module for seamless communication. Through the integration of wireless communication technologies, the system establishes a robust communication link between soldiers and the military command center. The collected data is transmitted wirelessly to the command center, where it is analyzed and evaluated by the experts. A Help button is added to the system for sending distress messages in case of emergency with location coordinates to the command center. Tracking of soldier locations enables efficient deployment of resources during critical situations, while monitoring of health parameters provides early warning signs for potential health issues.
Hybrid Beamforming Design for Beam-Hopping LEO Satellite Communications

Since the hybrid beamforming (HBF) can approach the performance of fully-digital beamforming (FDBF) with much lower hardware complexity, we investigate the HBF design for beam-hopping (BH) low earth orbit (LEO) satellite communications (SatComs). Aiming at maximizing the sum-rate of totally illuminated beam positions during the whole BH period, we consider joint beamforming and illumination pattern (BIP) design subject to the HBF constraints and sum-rate requirements. To address the nonconvexity of the HBF constraints, we temporarily replace the HBF constraints with the FDBF constraints. Then a joint FDBF and illumination pattern design scheme is proposed using random search and fractional programming (FP) methods. Based on the designed illumination patterns, we optimize the digital beamformers with the constrained analog beamformers by utilizing the FP methods, where a sum-rate maximization HBF scheme is proposed. Simulation results show that the proposed schemes can achieve satisfactory sum-rate performance for BH LEO SatComs.
Multimedia Retrieval in Mixed Reality: Leveraging Live Queries for Immersive Experiences

Recent advancements in Mixed Reality (MR) technology and the exponential growth of multimedia data production have led to the emergence of innovative approaches for efficient content retrieval. This paper introduces Mixed Reality Multimedia Retrieval ((MR)2), a groundbreaking concept at the convergence of MR and multimedia retrieval. At its core, (MR)2 leverages MR’s transformative capabilities with an innovative live query option, allowing users to initiate queries intuitively through real-world object interactions. By autonomously generating queries based on object recognition in the user’s field of view, (MR)2 facilitates the retrieval of similar multimedia content from a connected database. The technical backbone of the (MR)2 framework includes object detection (YOLOv8), semantic similarity search (CLIP), and data management (Cottontail DB). Our research redefines user interactions with multimedia databases, seamlessly bridging the physical and digital domains. A successful iOS prototype application demonstrates promising results, paving the way for immersive and context-aware multimedia retrieval in the MR era.
Sentiment Analysis of Amazon Reviews Using Machine Learning Classifier

Reviews can significantly impact a company’s reputation in the market, potentially influencing its overall business outcomes, either positively or negatively. This is especially crucial for companies that operate primarily through e-commerce platforms. Hence, it is vital for companies to pay close attention to customer reviews. Sentiment Analysis, often referred to as "opinion mining," is a significant procedure in Natural Language Processing (NLP) which serves the purpose of ascertaining the emotional tone of a provided text and categorizing it into positive, negative, or neutral perspectives. In this paper, sentiment analysis methodology is presented for classifying Amazon reviews which utilizes a large dataset of reviews and employs Multinomial Naïve Bayesian (MNB), Support Vector Machine (SVM), Maximum Entropy (ME), and Logistic Regression as the primary classifiers by the authors. With the aid of machine learning, we employed a supervised learning approach to an extensive Amazon dataset in order to categorize it based on sentiment polarity, achieving a high level of accuracy for the results. Here, we utilized the Kaggle dataset that includes a substantial volume of reviews and associated metadata which comprises customer reviews and ratings on Amazon products.
Efficient Wireless Federated Learning with Adaptive Model Pruning

For wireless federated learning (FL), this work proposes an adaptive model pruning-based FL (AMP-FL) frame-work, where the edge server dynamically generates sub-models by pruning the global model to adapt devices' heterogeneous computation capabilities and time-varying wireless channel conditions. To mitigate the negative effect of different structures of sub-models on learning convergence, this work designs a new compensating strategy for the pruned regions of sub-models via historical gradients. Since the freshness of gradients dominates the convergence speed, this work also defines an age of information (AoI) metric to characterize the staleness of the regions of the local gradients. Based on the compensating strategy, we formulate a joint device scheduling, model pruning, and resource block allocation optimization problem to minimize the average AoI for local gradients. To solve this problem, we theoretically derive an optimal model pruning scheme. After that, we transform the original problem into equivalent linear programming that can be solved with polynomial time complexity. Simulation results on the CIFAR-IO dataset show that the proposed AMP-FL outperforms the benchmark schemes with faster convergence speed and over 7% learning accuracy improvement.
Source Project Selection for Cross-Project Software Defect Prediction using Clustering Approach

Software Defect prediction (SDP) has an effective role in improving the software system quality. However, defect prediction models using local data have poor performance as the data available is inadequate. Multi-Source Cross-Project software defect prediction (MCPDP) uses the knowledge gained from source projects to detect the defects in the target project. In this paper, a novel clustering approach is proposed to select source projects for a given target project. The proposed approach is compared with existing source project selection strategies, such as Random selection, selection using Recommendation scores and using all available projects as source projects along with different classifiers, namely Naive Bayes(NB), AdaBoost and TSBoostDF. The performance evaluation is done using F1-score, G-mean, MCC and Balance. Wilcoxon Signed-Rank (WSR) hypothesis test is conducted to statistically compare the models. The statistical comparison shows that the proposed approach outperforms the existing source project selection strategies.
YOLOv5-Based Mask Wearing Status Detection

Face mask wearing detection technology can effectively supervise and control people wearing masks. The traditional mask detection uses only binary classification labels for training, which has low precision and recall. The mask wearing classification method is redefined, and a mask wearing detection method based on YOLOv5 is proposed. The model algorithm learns finer facial features by processing 24000 collected images with corresponding classification labels. Compared with the traditional two-classification method of mask wearing status, the YOLOv5 mask wearing detection method with multi-classification labels achieves 93.4% detection precision and 97% recall rate, which are improved to 3.1% and 2%, respectively, indicating that the new multi-classification labels of mask wearing can effectively improve the performance of mask detection.
A Narrowband Interference Cancellation Approach for Index Modulated OFDM Systems Using Compressive Sensing

This paper assesses the efficacy of Index Modulated (IM) schemes within Orthogonal Frequency Division Multiplexing (OFDM) systems under the influence of Narrowband Interference (NBI). Moreover, a novel mitigation strategy leveraging compressive sensing (CS) is introduced for OFDM-IM systems. The findings demonstrate that in contrast to conventional OFDM, OFDM-IM displays notable resilience against NBI up to a signal-to-narrowband interference ratio (SNIR) of -15dB. The expansion of NBI bandwidth exhibits a limited impact on performance degradation, whereas heightened interference power exerts a more pronounced effect. Broadly, in the absence of mitigation measures, error performance deteriorates and eventually stabilizes even at elevated SNR levels. Notably, under conditions of substantial interference power, the CS-driven NBI cancellation technique enhances the performance of OFDM-IM schemes, resulting in a substantial reduction of the error floor.
Arduino Based Smart Vacuum Cleaner

Cleaning homes and the surrounding area is increasingly difficult with the current hectic routine. There are hoover cleaners available nowadays that need to be operated by people. Therefore, the implementation of a vacuum cleaner that operates automatically is necessary. Through the implementation of this research work, an effective cleaning technique has been developed. By employing this hoover cleaner, dangerous areas can be cleaned, lowering threats to people. Here, the motors, sensors, and other parts of the hoover were controlled by an Arduino board that was configured to do so. By programming the hoover cleaner to clean areas, adjusting the suction power, and even navigatingaround obstacles, the cleaning process may be highly customized and controlled in this way. A pipe is hooked to thebottle's mouth on the hoover cleaner, which is built with a CPU fan. Batteries power the entire apparatus.
Robotic Grasp Prediction for Selective Objects in a Cluttered Environment using Object Centric Masking

Intelligent grasping is a very active field of research in robotics. In the current scenario, a robot can identify the object pose (position and orientation) and pick it up by predicting a grasp rectangle but cannot predict the grasp parameters for a particular class of object out of multiple objects in a cluttered environment. Also, the architectures used for predicting grasp parameters usually suffer from the problem of finding regions of interest (ROI) in a large-size image or image with multiple objects, which deteriorates the prediction quality. To solve this shortcoming, we have proposed a method to identify the ROI and concentrate the grasp prediction model on the ROI. We have used a state-of-the-art object detection model YOLOv5 and grasp prediction model GR-ConvNet as major components of our method. The YOLOv5 network is used to localize the object of interest in the scene and outputs the position coordinates for the object of interest. We propose a novel center-passing mechanism for GR-ConvNet, which considers the object location as reference while pre-processing the image. It uses a white mask technique for isolating the object of interest. With our white masking approach, we are claiming an accuracy of 96.25% on Cornell Dataset. Our method is also tested in a real-world scenario for two objects: a duster and screwdriver in a cluttered scenes. Experimentally we have demonstrated a successful grasp execution on a 7 DOF Baxter robot with 87.80% and 79.07% accuracy on sparsely and densely placed objects respectively.
A Semi-supervised Convolutional Autoencoder Framework using Transfer-learning based Pseudo-label Generation for Glaucoma Diagnosis

Glaucoma is a retinal disease that causes irreversible damage to the Optic Nerve Head (ONH) and can result in partial or complete blindness if left untreated. Early detection and treatment of the disease can alleviate such serious effects. Computer Aided Diagnosis (CAD) is gaining importance as an aid to clinicians in scenarios such as mass screening. With the advances in computational resources, the use of Deep Learning (DL) techniques in CAD systems is increasingly preferred due to DL’s capability of extracting high-level features automatically. However, the training of a DL model requires a large number of annotated data. Nevertheless, data pertaining to medical domain is mostly unlabelled, with a relatively small percentage of labelled data, and glaucoma is no exception. The proposed work aims to develop a DL-based CAD system for glaucoma diagnosis using retinal fundus images by integrating approaches including semi-supervised learning, transfer learning and pseudo-label generation for effective use of unlabelled data. Specifically, a Convolutional Autoencoder (CAE) is first trained in an unsupervised manner using unlabelled data. Secondly, a transfer learning model is fine-tuned with limited labelled data available and used for predicting pseudo-labels of the unlabelled cases, which are fed to the autoencoder model for feature extraction. Subsequently, classification is performed for glaucoma detection via a Support Vector Machine (SVM) classifier. The experiments conducted on the combined dataset comprising fundus images from ORIGA and DHRISTI-GS has yielded an accuracy of 90% for the proposed approach by utilizing a large unlabelled dataset and minimal labelled data, showing the efficacy of the approach in the absence of abundant labelled data.
The Local Observed Trends and Variability in Rainfall Indices Over the Past Century of the Yom River Basin, Thailand

강우 패턴의 변화는 홍수나 가뭄과 같은 극한 수문 사상 의발생빈도에 직접적으로 영향을 미칠 수 있으며 물 공급이나 홍수관리 등과 관련하여 이해당사자들 간의 분쟁을 초래할 수 있다. 본 연구의 목적은 과거 90년(1921~2015)의 기간을 대상으로 Yom강유역에 대한 강우량의 변동성을 분석하는 것이다. 이를 위하여 13개 수문관측소의 강우 자료를 기반으로.
Dual Band G-Slot 5G Antenna for Wireless Body Area Network

This study presents a dual-band microstrip patch antenna for wireless body area network (WBAN). This single antenna is capable of covering the two most common frequencies. The patch antenna is made up of a G-slot. The antenna is integrated on top of the Rogers RT 5880 substrate. The substrate’s dimensions are 12.5mm x 11.5mm x 1.57mm. The proposed antenna has resonance frequencies of 28 GHz and 39 GHz. The operational bandwidths for 28 GHz and 39 GHz are 12.73 GHz and 8.612 GHz, respectively. The antenna radiates with a maximum gain of 6.1 dBi at 28 GHz and 6.6 dBi at 39 GHz in free space. This antenna has a radiation efficiency of 64% at 28 GHz and 80% at 39 GHz. On the body, there was some performance decline. The antenna effectiveness reduces from 64% to 61% for 28 GHz and from 84% to 76% for 39 GHz due to the lossy feature of the body.
Energy-Efficient Heterogeneous Computing via Normalized Performance based Proactive Boost for Embedded Artificial Intelligence

Artificial intelligence enabled services are increasingly required for embedded systems. Accordingly, their computing power demand is exponentially increasing. To deal with that, one of the essential technologies is heterogeneous computing. However, the heterogeneous computing requires the excessive amount of energy consumption, since it extensively exploits all available computing units. Thus, it is crucial to reduce the energy consumption in energy-constrained embedded systems. Numerous dynamic voltage frequency scaling mechanisms have been widely investigated to reduce the huge amount of energy consumption. However, they cannot satisfy the desired time of neural network models. To address this limitation, we propose a normalized performance based proactive boost for heterogeneous computing. It dynamically lowers the operating frequencies of all computing units, satisfying the desired time of all neural network models at run-time. We implement our approach into Exynos 2200. We validate the effectiveness of the proposed mechanism through extensive experiments. Our experiments show that the proposed mechanism reduces the energy consumption of all computing units by up to 15.2 percent when compared to that of our earlier work. In addition, our approach incurs negligible run-time overhead.
Ship Detection in Satellite Images using You Only Look Once-Neural Architecture Search

Ship Detection in remotely sensed satellite imagery is a challenging task due to tiny size of the objects and low resolution of the images. Several object detection methods that perform very well for medium-sized and large-sized objects, miserable fail to perform decently in such applications. From the vast variety of the models available, few You only look once (YOLO) have proven to yield decent efficiency and accuracy in ship detection in satellite images. Hence, the paper proposes to detect and classify ships in remotely sensed satellite images by applying transfer learning to fine-tune the latest YOLO-Neural Architecture Search (YOLO-NAS) model. To the best of our knowledge this paper is the first attempt to test YOLO-NAS model for ship detection in satellite imagery. To demonstrate the strength of the YOLO NAS model, the paper also trains YOLOv5 and YOLOv8 on the same dataset allowing to comprehensively compare the efficiency of the proposed model against previous state-of-art versions. The findings demonstrate high mean average precision scores for the proposed model, indicating the effectiveness of the YOLO-NAS algorithm in accurately locating ships in satellite images.
Mobile Application to Collect Data and Measure Blood Component Level in a Non-Invasive Way

The increasing advancement in mobile technology has led to many smartphone-based technologies being widely accepted in various research fields. In recent years, promising smartphone-based technologies have been developed for activity recognition, gait analysis, telemedicine, and fitness tracking. However, some invasive methods are used for measuring blood components (hemoglobin, glucose, bilirubin, etc.). This invasive method involves drawing blood from patients and then analyzing the blood sample. This process is painful, and the time delay between the blood collection and analysis can be a significant disadvantage. Extensive research is being conducted to introduce a noninvasive method of measuring blood components. However, these methods may be expensive and inconvenient for people with little literacy. To deal with these issues, we have developed a mobile application that any group of people can use conveniently and quickly. Furthermore, this application can collect data without causing inconvenience and output blood glucose and hemoglobin levels, allowing for real-time data monitoring. A study evaluated the system’s performance, comparing invasive and noninvasive hemoglobin measurements on 15 subjects (11 male, 4 female) using this app. It was also tested on Samsung, Realme, Vivo, and Redmi phones, showing compatibility with diverse brands.
Joint Communication Resource Allocation and Velocity Optimization in Advanced Air Mobility via Multi-Agent Reinforcement Learning

In response to traffic congestion in urban areas, the National Aeronautics and Space Administration promotes the concept of Advanced Air Mobility (AAM), which envisages a safe and efficient air transportation system. However, the increased communication demands in AAM can exacerbate the spectrum scarcity issue, so a new communication paradigm is necessary. In this paper, we consider multiple aerial vehicles (AV) flying along their shortest paths for cargo/passenger delivery. During the flight, each AV must make decisions in every time slot on communication resource allocation and velocity selection under safety constraints. Accordingly, we formulate the joint optimization problem to minimize the weighted sum of the total travel time and communication outage time. The optimization problem is formulated as a Markov game and a multi-agent reinforcement learning based algorithm is proposed. Simulation results corroborate the effectiveness of the proposed solution.