Miracle-dz/UniFAR-FTUs huggingface dataset:
Non-isolated Multiport Converter for Electric Vehicle Applications

Power electronics systems must be effective and flexible due to the growing acceptance of electric vehicles (EVs) as a viable option for sustainable transportation. When delivering peak power, batteries perform less efficiently. On the other hand, ultracapacitors have a smaller energy storage capacity but can manage peak power. As a result, ultracapacitor and battery-based storage solutions are being researched. There are numerous configuration options and control strategies available for various systems. Due to their capacity to incorporate various energy sources, Multi-Input Converters (MIC) are the technological advancement appropriate for sustainable energies. In this paper, an adaptive neuro-fuzzy inference approach and a hybrid power management strategy are proposed. The suggested model was developed using MATLAB and Simulink. The simulation's findings demonstrate that the proposed strategy can provide the needed power to complete a typical driving cycle.
Photocatalytic Activity of Sonochemically Synthesized GO-Ag Nanocomposite

The demand of nanomaterial in various applications led to the development of many nanocomposite materials. Graphene oxide - silver nanocomposite is one such material which is synthesized due to the excellent properties of graphene and silver nanoparticles. The combined properties of graphene and silver nanoparticles are useful in the antibacterial activity, waste water treatment, dye detection, etc. Many researchers have developed the graphene oxide -silver nanocomposite for various applications which made it remarkable. In this article, the photocatalytic activity of the graphene oxide silver nanocomposite has proved to be the best compared to the pristine GO and AgNPs.
Leveraging Teacher Ensembles to Mitigate Label Noise in Clustering-Based Unsupervised Domain Adaptation for Person Re-Identification

Person re-identification (re-ID) pertains to the task of establishing correspondences between individuals across disparate camera views and holds relevance in the fields of surveillance and security. Recently, unsupervised domain adaptation (UDA) received lots of attention for person re-identification (re-ID). The prime objective of UDA is to enhance the performance of models by transforming knowledge of the source domain to the target domain, reducing the dependence on labeled data. This paper proposes an efficient approach that leverages teacher ensembles to mitigate label noise in clustering-based UDA for person re-ID. The proposed method involves transferring the knowledge from multiple teacher models to a student model, enabling effective adaptation while minimizing the reliance on labeled target domain data. Extensive experiments are conducted in prominent re-ID datasets to evaluate the impact of different factors, such as teacher network architecture and ensemble size variation for the proposed scheme. From the experimental results, it has been noticed that a well-selected teacher ensemble and an optimal ensemble size significantly contribute to improving the performance of clustering-based UDA. The proposed scheme brings more competitive performance gains than that of the state-of-the-art re-ID approaches. Further, this work contributes to the advancement of UDA techniques in person re-ID by addressing the challenges posed by label noise and showcasing the potential of teacher ensembles in enhancing clustering-based UDA performance.
Hybrid Spectral-Spatial Convolutional Network and Transformer with Mixup Regularization for Hyperspectral Image Classification

Existing deep learning-based models for hyperspectral image classification (HSIC) may be suboptimal in the utilization and the balance between the spatial and spectral information, and they pay more attention to the design of architecture and modules but ignore the generalization of the model on the data with slight distribution shift. In this paper, a novel framework based on spectral-spatial convolutional Transformer and Mixup regularization (SSCT-M) is proposed for hyperspectral image classification. SSCT-M designs a dual-branch architecture to extract spectral and spatial features and adopts a Transformer-based network to adaptively balance the contribution between spectral and spatial pipelines. Then, SSCT-M adopts a two-stage mixup regularization to supervise the learning of the model, which can boost the generalization performance of the model by learning convex combinations on the labeled data while encouraging the model to make consistent predictions on the perturbed unsupervised data. Extensive experimental results on two HSI benchmarks demonstrate the effectiveness of our proposed framework.
Analysis and Comparison of the DNA Sequence of an Unknown Respiratory Virus with Influenza and Corona Using Biopython

Influenza and coronaviruses are the most contagious and deadly respiratory viruses. In the past, influenza caused some of the worst pandemics in history, like the Spanish flu or the recent swine flu. It comes back every season, like a seasonal virus. COVID-19 has shown us how a not-so-deadly but contagious virus can affect us so badly. Scientists observed that both viruses can go through genetic mutations very quickly, and many variants of these viruses are living in different types of animals that can infect humans. It raised concerns about these viruses and the genetic mutations they cause. The main goal of this proposed method is to determine whether a new virus is a descendant or mutation of any previous viruses if a new type of illness emerges. With the use of machine learning, this study has compared influenza and COVID-19 with a variety of viruses to determine their similarities. The limitations in identifying different variants can be overcome with this procedure using biopython.
Enhancing Action Recognition with Channel Attention Modules in GCN

Skeleton-based GCN methods have found widespread application in the field of action recognition. ST-GCN++ represents a significant advancement over the ST-GCN method, resulting in a remarkable enhancement in performance. However, when dealing with complex action data, there are still potential issues related to information loss and redundancy. To address these challenges, we incorporate the channel attention module into ST-GCN++. Specifically, we introduced Se Blocks into each branch of the MSTCN module. After experimental validation on the NTU60 dataset, our model has achieved state-of-the-art (SOTA) performance levels in the current research domain.
Reliable Patient Data Securing for Medical Image Segmentation with Different CNN Architectures

Image segmentation is an essential task in the field of medical image analysis such as the segmentation of carcinogenic foci. However, since these images contain the patient's confidential information, there is a growing concern for protecting them from unauthorized access. With the widespread use of digital medical imaging systems, medical images are now stored in a centralized information system that is accessible to multiple users. In order to prevent unauthorized access and hacking attempts, this paper proposes a segmentation technique of carcinogenic foci in medical images combined with an encryption algorithm for data relating to this segmentation operation. The suggested method is divided into two major steps. For the segmentation step, a comparative study is conducted between some architectures of convolutional neural networks such as Unet and AlexNet. In fact, the architecture, which provides better segmentation results based on several criteria, is adopted for this work. A suitable algorithm is used for the encryption step to ensure the confidentiality of patient information such as patient name, calculated cancer area, exact disease area and direction of spread. The proposed technique ensures that the medical images are segmented and encrypted prior to being uploaded to the information system. This would allow only authorized personnel to access the medical images, thereby safeguarding the confidentiality and privacy of patient information. The obtained result on real data is interesting and proves the efficiency of the proposed method.
Certain Investigations on Hand Gesture Recognition Systems

In recent years, hand gesture recognition has played an important role in human-computer interaction (HCI). The field of HCI has extensively used this technology to improve the user experience. It is a constructive method of interacting with machines that has applications across several areas, such as virtual reality, robot control, deaf-mute communication, medical applications, human-computer interface (HCI), and others. Gestures may originate from a variety of human body parts, but most of them arise from the hand or the face. The methods used in research that use hand gestures range widely, including sensor-based, vision-based, depth-based, motion-based, and skeleton-based methods. A gesture recognition system's primary focus is to make it simpler to create a realistic model that can realize human gestures and utilize them to manage electronic devices. This study analysed the approaches, algorithmic techniques, and models that recognize hand motions, which include Dynamic Time Warping (DTW), Support Vector Machine (SVM), K Nearest Neighbours (KNN), Artificial Neural Networks (ANN), Convolution Neural Network (CNN), and Hidden-Markov models (HMM).
Leveraging towards Multi-objective IoT Application Placement in Fog Computing Environment

Internet of Things (IoT) is one of the most talked-about innovations of recent years because it paves the way for novel interactions between devices and people that can improve daily life. Interconnected devices are generating an unprecedented amount of data due to the growth of IoT devices. When it comes to processing the data of IoT applications, a new paradigm known as fog computing is developing as an intriguing alternative. To leverage the maximum benefit optimal deployment of services into the fog computing devices has paramount importance. To find the optimal placement of services into the heterogeneous fog computing is a NP complete problem. In the present work, we implement genetic algorithm with Multi-objective IoT applications placement to minimise the three important parameters i.e. makespan, energy and cost. It also presents a comparison with the placement of single objective IoT applications. The findings indicate that employing Multi-objective placement approaches yields greater benefits in comparison to utilising a single objective placement strategy.
Multivariate Time Series Forecasting to Forecast Weight Dynamics

This paper represents a multivariate time series forecasting approach to predict the weight dynamics of an insect colony. In this paper, we are predicting the Colony Weight of the Insect which has the main impact on the growth and overall health of the insects. To accomplish this task, we have performed extensive preprocessing on the time series data, ensuring data quality and suitability for analysis. Overall, we have conducted a high-level Exploratory Data Analysis which covers time series analysis to identify different patterns as well as trends present in the time series data. Following the EDA, Feature Engineering and Feature Selection techniques have also been performed to find the features that have an impact on the colony weight feature. As a follow-up, we have performed Multivariate Time Series forecasting of the colony weight for future data with Time Series Forecasting techniques such as Vector Autoregressive moving average with exogenous regressors (VARMAX) and with Deep Learning techniques such as Long Short-Term Memory (LSTM). Several statistical parameters such as AIC score, BIC score as well as MSE value along with the grid search method have been utilized to find the optimal lag value which affects the model performance significantly. The performance of the black box model has been compared with the interpretable model to find the best outcome. This paper contributes to the field of multivariate time series forecasting in the context of insect colony dynamics. Various insights that have been gained from that study have implications for understanding insect behaviour, population dynamics, and overall ecosystem health. Furthermore, the proposed methodology can be extended to other domains involving multivariate time series forecasting.
Estimation of discharge voltage in lithium-ion batteries (LIB)

Discharge profiles in Lithium-Ion batteries allow the extraction of information about their behavior in the face of different variables such as load, operating time, temperature, among others. One of the ways that initially allows describing the operation of the battery is the output voltage at its terminals. It is possible to expand the capacity of the accumulator and extract more from its operating conditions. This paper presents the modeling of the 18650 lithium cell at different discharge profiles. Initially, the extraction of the current, voltage, and temperature variables from the LG18650HG2 dataset was carried out, which was preprocessed and normalized. Then different deep neural network models were implemented, obtaining a base model and an evaluation model. The first results of the predictions demonstrated better performance of the deep neural network with the Huber function, which only required 50 epochs to obtain losses of less than 2%. With hybrid models, which combine several computational intelligence techniques, the performance of predictions measured as a function of MSE and variance was improved.
Commandable areas of a Two-input three-level buck converter

An electrolyzer is a device which is used to produce hydrogen. This device requires a low voltage to operate. One way to reduce DC voltage is utilizing a two-input three-level buck converter. The challenge in controlling this type of converter is that sometimes the generated duty cycle located outside the interval between zero and one. Therefore, identifying the commandable areas for proper use of these converters is essential. In this article, by using the rules governing this converter and state equations, the commandable areas of the converter and its four switches have been identified by simulations results.
Mapping of flooded vegetation by means of polarimetric Sentinel-1 and ALOS-2/PALSAR-2 imagery

ABSTRACT This article presents for the first time the combination of dual-polarimetric C-band Sentinel-1 synthetic aperture radar (SAR) data and quad-polarimetric L-band ALOS-2/PALSAR-2 imagery for mapping of flooded areas with a special focus on flooded vegetation. L-band SAR data is well suited for mapping of flooded vegetation, while C-band enables an accurate extraction open water areas. Polarimetric decomposition-based unsupervised Wishart classification is combined with object-based post-classification refinement and the integration of spatial contextual information and global auxiliary data. In eight different scenarios, focusing on single datasets or fusion of classification results of several ones, respectively, different polarimetric decomposition and classification principles, including the entropy/anisotropy/alpha and the Freeman–Durden–Wishart classification, were investigated. The helix scattering component of the Yamaguchi decomposition, derived from ALOS-2 imagery, showed high suitability to refine the Sentinel-1-based detection of flooded vegetation. A test site at the Evros River (Greek/Turkish border region) was chosen, which was affected by a flooding event that occurred in spring 2015. The validation was based on high spatial resolution optical WorldView-2 imagery acquired with short temporal delay to the SAR data.
ANN based SEPIC Converter for Electric Vehicle Charging

In this paper, a Single Ended Primary Inductance Converter (SEPIC) topology with Power Factor Correction (PFC) is presented as a method for charging the battery of an electric vehicle. This converter offers advantages such as reducing the total number of components and reducing the conduction loss during AC to DC conversion, leading to a higher overall system efficiency. The working functionality of the SEPIC-PFC converter is enhanced by using Proportional Integral (PI) controller. By using supercapacitors to collect energy, the batteries experience less stress, resulting in longer battery life and lower costs. The Multi Source Inverter (MSI) is deployed for converting DC voltage into AC voltage, and it also controls the flow of electrical power. The recommended MSI for Electric Vehicle (EV) battery system with ANN Feedback line controller is designed to enhance the effectiveness of the suggested approach by managing the State of Charge (SOC). By integrating an ANN feedback line controller for bidirectional DC/DC converter, energy management is attained with enhanced battery charging and discharging. Based on simulation results attained in MATLAB/Simulink, the proposed control technique is shown to perform well and is robust. The suggested method demonstrates that, with the offered sufficient training data for the ANN models, highly precise SOC estimated outcomes are accomplished.
Deep Learning Approaches for Crowd Density Estimation: A Review

The term “crowd density estimation” refers to the process of determining the number of people in a certain region or a specific area. This method has a wide range of applications, including urban planning, healthcare, emergency response, public safety, and strategic planning. Occlusion, size and perspective distortion, and uneven distribution all pose problems for crowd counting systems. Calculations grow increasingly complex as population density increases. The considerable contributions of deep convolutional neural networks (CNNs) and developments in datasets have contributed to the notable progress in crowd counting approaches in recent years. This study examines both standard and deep learning-based crowd counting approaches in depth. It investigates detection-based, regression-based, and traditional density estimate methods. The 10 most recent works on crowd counting using deep learning are reviewed, with an emphasis on estimating crowd density and count from available photos. The research also looks at regularly utilized datasets. The findings of these investigations indicate a high level of precision, demonstrating the promise of AI in crowd counting. Notably, the approaches and algorithms used to handle the issues of crowd counting and density mapping vary significantly between articles. The research investigates prospective uses of crowd counting as well as the accompanying obstacles. Various crowd counting models and methodologies are examined, stressing the range of approaches to dealing with the problems of crowd counting and density mapping.
Social Media Analytics for Disaster Management using BERT Model

Disasters, both natural and human-induced, continue to affect the global population in multiple ways. In the past decade, social media has evolved and has seen a rapid, exponential increase in the number of users and their data. The manner in which communities anticipate, react to, and recover from natural and man-made disasters have been entirely shifted by rapid information transfer, public sentiment analysis, and real-time communication. There is now a high demand for social media based disaster management systems. This research project introduces a sentiment analysis framework tailored to Twitter data, incorporating BERT as a core component. Additionally, we employ a range of NLP methodologies for evaluation and comparative analysis. The results indicate that the employment of BERT yields a noteworthy enhancement in accuracy, surpassing alternative models by 5% to 7%. In summary, our research accentuates the effectiveness of employing BERT-based sentiment analysis to optimize disaster management strategies.
Bio-inspired artificial intelligence robots: current challenges and potential research directions

Intelligent soft robotics is a relatively new yet satisfying technology that combines the advantages of major advances in embodied intelligence with the natural softness of robotic structures. The field of soft robotics aspires to equip robots with novel, bio-inspired qualities that allow them to structurally adjust to various environments. This study takes a mechanical approach to bioinspired soft robots by discussing the mechanics behind material selection, actuation, and design. Soft materials are necessary for biologically inspired robots' safe interaction and general functioning. Soft robots, which can learn via experience, have the potential to reduce mechanical and computational complexity. Creating completely autonomous, self-learning agents is now feasible by combining deep reinforcement learning (DRL) algorithms with a soft, bio-inspired architecture. Developing the mathematical models needed to train soft robotics utilizing deep reinforcement learning (SR-DRL) agents in soft robotic systems with numerous degrees of freedom. Using cutting-edge imitation learning algorithms in soft robotic systems has shown encouraging results. This article overviews many such algorithms and illustrates their use using examples from actual applications. The experimental outcomes show that the suggested SR-DRL system achieves a high accuracy ratio of 96% and a reliability ratio of 89% compared to other existing models.
Research on Pedestrian Detection Algorithm Based on Improved YOLOv5

In response to the random distribution and dynamic characteristics of pedestrians, as well as the false alarms and omissions in the pedestrian detection process, this paper proposes an improved pedestrian detection algorithm based on YOLOv5s. The proposed algorithm incorporates an attention mechanism module by replacing the four C3 modules in the Backbone with CBAMC3. This is done to enhance the network's ability to extract pedestrian target features. The Concat module is replaced by BiFPN to optimize the performance of object detection. Experimental results show that the improved algorithm in this paper has improved Precision, Recall, and mAP compared to directly using YOLOv5s. This indicates that the improved algorithm has significant enhancement effects and can meet the requirements for pedestrian detection, thus having important application value.
Study of high-current press-fit connectors for electrical performance

Connectors for bringing high currents in and out of circuit boards are undergoing a significant evolution to meet the contemporary demand across various applications, including electric automotive, renewable energy, and automation. The development of high-current connectors has advanced considerably, moving away from traditional soldering methods to modern solderless press-fit connectors. This study subjects two high-current connector types, namely with solid press-fit and compliant press-fit pins, to a comprehensive evaluation based on both mechanical and electrical parameters. An experimental setup was meticulously prepared to measure the press-in force in relation to displacement. The choice of one connector over the other is determined mainly by application, but factors such as geometry, material and press-in forces can be equally important. Given the growing requirements for higher current and power capacities even in everyday applications, the impartial performance evaluation of these connectors becomes imperative for a deeper understanding and their effective use in various domains.