Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploiting QUIC Multi-Streaming Over NTN: Delay-Based Scheduling Policies

We analyze the performance of QUIC over Non-Terrestrial-Networks (NTN). The presence of this transport protocol has strongly increased since it was originally proposed, but its behavior over wireless networks is still an open question. We focus on NTN, which will play a key role in forthcoming cellular systems, and whose underlying connectivity exhibits a highly variable behavior. We show that using multiple streams, which is one of the most relevant advantages of QUIC, yields performance improvements. We then propose a delay-based scheduler, based on dynamic queuing control, and we compare its behavior with that exhibited by legacy solutions. We carry out an extensive experiment campaign exploiting a novel methodology that combines virtualization techniques, real QUIC implementation, and ns-3 to model NTN links. The results show that the proposed scheduling policies fairly distribute the delay between streams, without jeopardizing the throughput, even under very high load situations.
Integrating Demand Response Technique with Effective Control Algorithm for Energy Management System in a Renewable Energy Based-Micro-grid

This paper addresses the challenges of demand response (DR) in energy management, especially within microgrids. It emphasizes the need for effective control strategies and market structures to maximize DR resource potential. The intermittent, stochastic, and distributed nature of generation and consumption patterns requires novel control algorithms. Therefore, this paper investigates the demand response technique for the energy management system in a micro-grid based on adaptive model predictive control (AMPC). More so, the objective of the DR technique in this paper is to use the available renewable energy resources optimally, maximize the economic benefit, reduce the peak load demand, and manage load consumption patterns, improving micro-grid operation. The simulation results from the MATLAB/Simulink environment have shown that implementing the DR technique for energy management in microgrids reduces the peak load demand and, consequently, minimizes the operation costs of the system.
Equalization in 5G and Beyond: When is Interference Rejection Combining Really Helpful?

The need for higher uplink data rates in 5G and beyond systems is hindered by co-channel interference that can arise either from the neighboring cells or due to the co-channel deployment of macro and low-power base stations. To mitigate this issue, MMSE with interference rejection combining (IRC) has been widely adopted in practical systems. Unfortunately, MMSE-IRC suffers from higher complexity (cubic in number of BS antennas) compared with traditional MMSE equalizer (cubic in number of users). This is particularly concerning given the large number of BS antennas used in 5G and beyond systems. Further, it is illustrated in the present work that, under certain practical constraints, MMSE-IRC shows either the same or even worse performance compared with MMSE. Based on these observations, the present work proposes an opportunistic receiver that switches to MMSE from MMSE-IRC under favorable conditions, thereby reducing the average complexity without compromising in performance. The proposed receiver is validated through 5G link level simulations under practical channel models, demonstrating a significant reduction in complexity compared with MMSE-IRC.
The Effect of Redundant Paths on Internal Blocking of Multistage Interconnection Networks

In this paper, we discuss the Multistage Interconnection Networks (MIN) network which is named RIN (Reliable Interconnection Network) with the size of $8 \times 8$. This RIN network is very similar to the original Gamma network. It’s just that this RIN has four disjoint paths for each input-output connection. From the analysis carried out with the first permutation, the result was that all input-output connections were successfully carried out without any packets experiencing blocking. In the second permutation, blocking occurs at stage 1 but the packet can still reach its destination via another route after backtracking. In the third permutation, the point to multipoint connection (broadcast) was successful even though initially after the first redundant path was full the packets switched via another input terminal which also had four redundant paths, so that finally all packets could reach the output terminal without blocking.
A method for predicting bone age using Vision Transformer

Based on the principle of TW3 integration, this paper presents a high level of automatic bone age evaluation algorithm with high interpretation and clinical applicability. A hybrid network known as SMTNet was developed to investigate the classification of bone maturity in TW3 clinical scoring regions. The objective of this study is to obtain bone age results for the metacarpal (RUS) and carpal (C series) by including appropriate bone maturity scores. This method was trained and tested using a dataset of 5000 children’s left hand X-rays provided by a tertiary hospital in Beijing. The experimental results show that the average absolute error of the C-series bone age model is 0.52 years, while the RUS series is 0.51 years. To sum up, the method of bone age assessment presented in this paper has been successfully applied in many clinical settings.
Utilizing Deep Learning Architectures for Early Detection of Lung Diseases in Chest X-ray Images

In addressing the critical need for early detection of COVID-19 and viral pneumonia, our study introduces an advanced system using deep learning for analyzing chest X-ray images and their respective lung masks. Utilizing the comprehensive COVID-QU-Ex dataset, we extensively trained and evaluated five pre-trained deep learning models (ResNet-18, VGG-16, AlexNet, Inception-V3, and DenseNet-169) and developed a novel ensemble model. This ensemble model, combining the strengths of the top-performing models, achieved a notable 96.002% prediction accuracy and 95% recall, outperforming the individual models. Our research goes a step further by integrating explainable AI methodologies, specifically LIME and SHAP, to provide understandable and transparent explanations of the model’s decision-making process. These explanations are crucial for identifying critical areas of lung infections and enhancing the model’s credibility. Furthermore, we deployed a user-friendly Flask web application, enabling easy access to our system for medical professionals. This application offers immediate analysis of uploaded X-ray images, delivering predictions, accuracy percentages, and interpretative visual explanations. Our work significantly advances the field by providing an accurate, explainable, and accessible tool for early lung disease detection, potentially revolutionizing the approach to healthcare diagnostics.
Refining Ground Classification for the Distribution of LTE Users Using Supervised Learning Techniques

Several studies have shown that the layout of an area's infrastructure has a strong impact on the mobility of the mobile network users. Each district in a city has one or more different type of activity areas. Depending on the type of activity areas that a district covers, several profiles emerge. These profiles are closely linked to the impact a district can have on LTE users mobility. In the current work, we propose a first approach to determine the profile of a district in a territory. The territory under study is the city of Lomé, the data used for this analysis come from the geographical data of the OSM database. An alternative approach is proposed that, in the case of missing data, determines the profile of a new district from knowledge built from other districts in the same study area. To validate the proposed approach, evaluations were conducted considering several types of distance (Mahalanobis distance, Euclidian distance,…). It appears that with the K-NN algorithm, using manhattan distance, we have 61 % accuracy in determining the profile of a new district based on the nearest district's profiles.
Optimizing Software Release Management with GPT-Enabled Log Anomaly Detection

As software systems become increasingly complex, detecting anomalies in log data is crucial to ensure high performance and stability. In this paper, we propose a novel approach to log anomaly detection using GPT-3 language models. We utilize the word embedding and tokenizer capabilities of GPT-3 to transform log data into a language model that can identify unusual patterns and anomalies. Our proposed method can be integrated with software release management processes to automatically detect anomalies and improve quality control. By leveraging GPT’s ability to capture complex patterns and relationships within the data, our approach achieved an accuracy of 99.75%, 99.00%, 98.75%, and 99.33% on real-world datasets of Apache, BGL, HDFS, and Thunderbird respectively, outperforming traditional methods of anomaly detection. Our experimental results on real-world log data demonstrate the effectiveness of our proposed approach, making it a promising tool for software release management teams to streamline their processes and ensure the highest level of system performance and reliability.
Degradation of persistent endosulfan by isolated actinomycetes and Rhodococcuss jostii, RHA1

The property of organochlorine endosulfan which is persistent and toxic can cause environmental pollution and pose human health and has raised concern worldwide. Hence, biodegradation serves as promising tool in overcoming the problem of pesticides pollution. The effective approach of removal of endosulfan from the environment is by using Gram-positive microorganisms such as actinomycetes, predominant microorganisms found in soil habitat are less exploited and studied in biodegradation of endosulfan. The diverse metabolic diversity and unique enzymatic capabilities of actinomycetes has gained interest in biodegradation to remove the endosulfan from the environment. This study aims to isolate indigenous actinomycetes from soil habitat and screening the ability of isolated actinomycetes to degrade the endosulfan. Cameron Highland where intensive agricultural activities were carried out was chosen as the sampling site for isolation of actinomycetes that could have potential in degradation of endosulfan. Screening of the ability of endosulfan degradation by isolated actinomycetes in sulphur free broth culture and followed by determination by residual endosulfan by using GC-ECD analysis which is sensitive and specific assay in detecting the endosulfan.
Typing on Any Surface: Real-Time Keystroke Detection in Augmented Reality

The ineffectiveness of text entry interfaces remains a significant barrier to social engagement in augmented reality (AR). Popular options, such as mid-air keyboard interface, wireless keyboards or voice input face challenges such as ergonomic issues, limited accuracy, or social discomfort in public use. This paper introduces a deep-learning method allowing AR applications to predict keystrokes from the user perspective video stream captured by any AR headset. This enables users to type on flat surfaces without a physical or virtual keyboard. Our two-stage model combines an off-the-shelf hand landmark extractor with an innovative adaptive Convolutional Recurrent Neural Network (C-RNN). It was trained on a newly built dataset, enabling prediction of 27 keys (alphabet and space) at approximately 32 FPS. With practice, users can reach a 91.0% accuracy at 40 words per minute, comparable to typing on a physical keyboard. The encouraging results demonstrate our method’s feasibility and potential for integration in diverse applications. We also explore limitations and future research directions for production system implementation.
Discovering Hidden Knowledge and Optimizing the Model by Analyzing Linear Regression Assumptions

Linear regression is the most frequently used regression analysis due to its simplicity in predicting and forecasting. However, because of its parametric approach, it is necessary to test five assumptions before using this technique on a given dataset. Two of these assumptions are critical, such as linearity and multicollinearity. Linearity has significance for representing patterns; whereas, multicollinearity reduces the model’s interpretability. If the negligible level of linearity and the most extreme level of multicollinearity are known before applying regression analysis, it saves significant computing time. This study centers around the level of the greatest linearity and negligible multicollinearity expected for model success. Ordinary least squares (OLS) regression analysis has been done for this purpose. A thorough investigation of ten UCI datasets reveals that at least 70% linearity is required for the model to perform well, while a maximum of 45% multicollinearity is acceptable. Furthermore, feature engineering is used to enhance the efficiency of the model by prioritizing linearly associated significant features with the target variable and contains no or little multicollinearity concerns. This implies the final model is very close to the assumptions, lead to upgrade the performance of the model.
A Deep Reinforcement Learning Approach for the Placement of Scalable Microservices in the Edge-to-Cloud Continuum

The recent proliferation of computing paradigms, and among them the more prominent ones at the two endpoints of the network infrastructure, i.e. the edge and the cloud, paves the way for an edge-to-cloud continuum of resources and services that is able to meet the stringent Quality of Service (QoS) requirements of emerging applications. However, deploying modern microservice-based applications on such highly distributed and heterogeneous edge-to-cloud infrastructure is a complex challenge to be addressed. To overcome this challenge, we jointly investigate the placement and load distribution problems for applications composed of dependent microservices that can be deployed and scaled across the continuum. Furthermore, we propose a Deep Reinforcement Learning (DRL) based solution for solving this joint problem and we demonstrate through simulations that our proposal outperforms baseline methods in terms of QoS satisfaction and deployment cost.
Brain Multi-Region Information Fusion using Attentional Transformer for EEG Based Affective Computing

In this paper, we worked on the fusion of multiple brain regions in order to combine information from different brain regions. The idea is that considering the dynamic processing of emotional video stimulus will involve different brain regions, and hence, fusion of information from these brain regions can increase emotion recognition accuracy significantly. We utilized EEG datasets from Indian (DENS) and European (DEAP) populations, comprising 128 and 32 channels, respectively. We categorized EEG channels into different brain regions and divided them into five sub-regions. Then, we used five transformer models to extract information from these sub-regions and concatenated the results to obtain a fused information vector for classification. The idea resembles the theoretical findings that brain association areas that fuse information from different brain lobes contribute significantly to emotion processing. For the DENS dataset, we achieved 97.78% accuracy in valence and 95.74% accuracy in arousal. For the DEAP dataset, we achieved 90.07% accuracy in valence and 84.52% accuracy in arousal. We propose that deep learning models simulating information fusion in the association regions of the brain can enhance emotion recognition accuracy.
Comprehensive Analysis of Artificial Intelligence in Virtual Reality and Augmented Reality

As emerging technologies like artificial intelligence (AI), augmented reality (AR), and virtual reality (VR) continue advancing, they harbor immense potential to transform research and education in innovative ways. Historically, VR/ AR and AI technologies have largely progressed on separate trajectories, pioneered by distinct groups with different aims. However, these fields are now experiencing accelerated convergence and intermingling. This paper provides a comprehensive survey of state-of-the-art AR/VR approaches, pinpointing limitations addressable through integration with modern AI. We critically appraise the trade-offs between traditional AR pipelines and AI- infused alternatives for key computations. We propose an original integrative framework leveraging the complementary strengths of AR and AI to transcend the boundaries constraining conventional AR systems. Extensive benchmarking and experimental validation quantitatively demonstrate the benefits of fusing AR with AI, rather than relying solely on traditional non-learning techniques. This work synthesizes vital interdisciplinary insights at the cutting edge of artificial intelligence, virtual reality, and augmented reality.
Comprehensive study of Sign Language Conversion Using Machine Learning

In this world, communication plays a major role to interact with each other. It acts like a bridge to convey message to other people. Human beings are able to manage their tasks by making use of their five senses, in which hearing is strongest among them. Some people have hearing loss from birth which results in physical health issues, which makes it difficult for them to acquire and say new words. These kinds of physically challenged people are known as Deaf and Dumb. The people who have hearing or speech impairments use the sign language to convey their messages. A Sign language is a combination of the hand gestures, face emotions and body movements. By using the sign language deaf and dumb people can communicate and deliver their message to others. There are several different varieties of sign language, primarily American Sign Language and British Sign Language. Normal individuals, however, struggle to understand sign language and the significance of others' actions since they are unable to use it. Our motivation for this study is to know how normal people can easily communicate with people deaf and dumb who understands only sign language. Here we analyses of various papers and their methods for solving this problem are discussed. The main solution of this situation is to create models which are helpful to understand the meaning behind the actions. So many models are created with various approaches like SURF with SVM and CNN, Neural machine translation, Deep LSTM, Natural language processing and other techniques. This study is done across various approaches to know the performance of each model their advantages and disadvantages in detail.
Design and Optimization of an Area Efficient Ultra-Low Voltage Differential Ring VCO with Wide Tuning Range for PLL Applications

This article presents the design and analysis of a 90nm CMOS three-stage differential ring voltage-controlled oscillator (VCO) functioning at an impressively low supply voltage of 0.8V. The VCO demonstrates favorable power efficiency with a power consumption of only 3.2mW at 5GHz and 1.2mW at 2.4GHz. The tuning range extends from 0.396 GHz to 7.544 GHz, providing remarkable versatility for a variety of applications. The proposed structure exhibits a good phase noise characteristic, achieving -83.94dBc/Hz and -87.20 dBc/Hz at 5GHz and 2.4GHz, respectively, at an offset frequency of 1MHz. The calculation of the figure of merit (FOM) provides a comprehensive evaluation of the VCO’s performance, yielding values of -152.87 dBc/Hz for 5 GHz and -154.01 dBc/Hz for 2.4 GHz, indicating its robustness and suitability for precision frequency synthesis. The VCO occupies an exceptionally small amount of area, specifically 0.00067 mm2. In addition, the model’s performance is assessed, and its compatibility is examined through a comprehensive analysis involving process corner evaluation, temperature sweeping and Monte Carlo analysis across diverse environmental conditions. This study demonstrates that the proposed VCO has the potential to significantly enhance the effectiveness of Phase-Locked Loops (PLLs) and other frequency synthesis applications in power-sensitive and high-frequency communication systems.
A High-Precision Low-Dropout Regulator With High Current Efficiency and Slew-Rate Enhancement

A fast-settling Low-Dropout (LDO) regulator has been presented in this paper. A new frequency compensation scheme has been proposed to ensure stability for all loading conditions without using external compensation capacitors. The new LDO regulator has been designed and simulated in a standard $0.18 \mu \mathrm{m}$ CMOS process. The simulation results show that the LDO achieves 4.42MHz and 3.6MHz unity-gain frequency for $0 \mathrm{~mA}$ and $350 \mathrm{~mA}$ of load current, respectively, while the minimum phase margin is almost 60 degrees. From the results of the simulations, it can be seen that the worst-case settling time for the load step current from $0 \mathrm{~mA}$ to $350 \mathrm{~mA}$ is $5.2 \mu \mathrm{s}$, which is reduced to $1.5 \mu \mathrm{s}$ after using a newly proposed slew-rate enhancement circuit. Also, the settling time for the load step current from $350 \mathrm{~mA}$ to $0 \mathrm{~mA}$ is almost $3 \mu \mathrm{s}$. The LDO consumes a quiescent current of 16.5 $\mu \mathrm{A}$ and the power supply rejection is $-50 \mathrm{~dB}$ at 20kHz frequency offset.
Comparative Analysis of D/A Converter Architectures for SAR ADC

This study gives an in-depth analysis of the architectures utilized in the analog-to-digital conversion process. The paper encompasses the design, performance, and suitability of the binary-weighted (charge distribution), R-2R ladder, and C-2C Digital-to-Analog (D/A) Converter architectures. This paper's discussed D/A converter architectures are simulated through the cadence tool using 180 nm CMOS technology. Based on comparative performance analysis, the C-2C D/A converter gives optimum results in terms of power, speed, DNL, INL, and settling time while maintaining its resolution. C-2C D/A converter reported a 63.15% improvement in power consumption compared to R-2R DAC, DNL, and INL errors below 0.01 LSB.
Novel Nano-Machine Architecture for Machine Learning in the IoBNT

In this work, we propose a novel nano-scale architecture that performs matrix multiplications. Matrix multiplications are the basic operations of machine learning (ML) algorithms and, thus, the presented approach enables their application at the nano-scale, for example inside the human body in the Internet of Bio-Nano-Things (IoBNT). It is based on the molecule exchange between connected compartments and introducing chemical reactions in some of them. The matrix entries are solely defined by the volumes of the compartment. We provide a detailed mathematical description of the stochastic and dynamic behavior of the system. Moreover, we derive design guidelines for the proposed architecture. Finally, we validated the proposed approach through particle-based simulations.