Real Time Research Articles

Inflammatory Biomarkers in Patients With Type 2 Diabetes Mellitus and Heart Failure With Preserved Ejection Faction.

To verify the relationship between gene polymorphisms of tumor necrosis factor alpha (TNF-α) and interleukin-6 (IL-6) with inflammation markers and codependent metabolic variables in patients with type 2 diabetes mellitus and chronic heart failure (CHF). This study included 154 patients (mean age, 69.1±3.2 years). The control group consisted of 47 patients with metabolic syndrome (MS) without CHF; the 2nd group included 56 patients with CHF with preserved ejection fraction (CHFpEF); and the 3rd group consisted of 51 patients with CHF with reduced ejection fraction (CHFrEF). The rs1800629 polymorphism of the TNF-α gene (TNF-α: G308A) was studied in real time by the polymerase chain reaction (PCR) method and the rs1800795 polymorphism of the IL-6 gene (IL-6: 174 G>C) was studied by PCR with the electrophoretic detection. The frequencies of polymorphic alleles were compared with the clinical blood test results, plasma concentrations of C-reactive protein (CRP), TNF-α, leptin, and fibrinogen. Differences between the groups were determined using the F test. Relationships between individual studied parameters were identified using the regression analysis. In most patients, the occurrence of gene polymorphisms was eident as increased plasma concentrations of biomarkers. An association was found between the TNF-α gene polymorphism (G308A) and an increase in plasma TNF-α and between the IL-6 gene polymorphism (174 C>G) and an increase in plasma CRP. In the CHFpEF group, the rs1800629 gene polymorphism was observed in 55% of patients, among whom 93% had increased TNF-α. The rs1800795 gene polymorphism was observed in 82% of CHFpEF patients, among whom 21% had increased CRP. In the CHFrEF group, the G308A transition in the TNF-α gene was observed in 53% of patients; an increase in the respective cytokine was noted in 67% of patients; the IL-6 gene polymorphism 174 C>G was found in 78%, however, only 14% of patients with this polymorphism had also increased CRP. In the control group, the TNF-α G308A gene polymorphism was found in 30% of patients, while an increase in free TNF-α was associated with this polymorphism in 50% of patients; the IL-6 174 C>G gene polymorphism was detected in 78%, while no increase in the CRP level was observed in this group. This demonstrates a high probability of the TNF-α G308A gene polymorphism occurrence in patients with CHF. Inflammatory markers are important predictors of CHF. The most significant predictor was the TNF-α G308A gene polymorphism, which was observed in more than 50% of patients, the majority of whom had an increase in plasma TNF-α.

Specific antitumor activity of anti-CA125 CAR-T lymphocytes against CA125-positive and CA125-negative cells

Aim. To evaluate the antitumor efficacy of our developed drug based on cytotoxic T lymphocytes genetically modified with a chimeric antigen receptor (CAR) specific to the CA125 antigen in relation to both CA125-positive and CA125negative cell cultures.Materials and methods. We performed an in vitro study on CA125-positive human ovarian cancer cells (OVCAR-3, OVKATE) and CA125 negative cells (breast cancer MCF 7, embryonic kidney HEK293). Cytotoxic effects on tumor cells were evaluated after 0, 4, 8 and 24 hours using the 3’-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) and Lactate Dehydrogenase (LDH) tests. We also studied the changes in the number of cells “in real time” when exposed to transfected lymphocytes using the RTCA iCELLIgence device (ACEA Biosciences, USA). Lymphokineactivated killer (LAK) cells were used as a specificity control.Results. The study demonstrated that anti-CA125 CAR-T lymphocytes exhibited a pronounced cytotoxic effect on OVCAR-3 and OVKATE cell cultures, exceeding the effect of LAK by 1.3 times. The cell population in the experimental samples decreased by 70 ± 4%, which exceeded the LAK effect by 9 ± 8.2%. With regard to the MCF-7 cell line, the cytotoxic effect of anti-CA125 CAR-T lymphocytes was minimal as evidenced by a 25.8% decrease in the relative number of live cells in comparison to the LAK cytotoxicity of 68%. Real-time monitoring of cell proliferation and viability proved a high specific cytotoxic effect of anti-CA125 CAR-T lymphocytes against tumor cultures expressing CA-125, while inferior to LAK in cultures not expressing CA125 (MCF-7, HEK293).Conclusions. The use of anti-CA125 CAR-T lymphocytes against CA125-positive tumor cell lines OVCAR-3 and OVKATE demonstrated a pronounced specific cytotoxic effect exceeding the cytotoxic effect of LAK, which was not achieved against CA125-negative MCF-7 and HEK293 cells.

Analysis of Child Safety Monitoring System using the SPSS Method

A wearing children's safety system functions as a smart gadget. Accurate and exact real-time location can be used to identify a child's surroundings. This system includes an ambient temperature display, an SOS light, and a distress buzzer. It aids them in locating their child. The system is simple to design. The programmer is designed to establish an approach that is user-friendly on both sides. GPS-equipped smart phones should be available to both parents and kids. The child's location, call history, message history, and communication via their smartphone are all tracked using this software. Safety monitoring systems use a person, rather than a mechanical system (safety monitor), to warn when roofs are six feet or more below the lowest level and are in danger of falling. A safety supervisor must be a competent person to recognize fall hazards and warn workers about them. The device has IoT tracking and a GSM module that allows you to monitor the child at all times. It has countless sensors connected to a CPU and is used to detect the right signals like heart rate, temperature and other dangers and alert the parents. A programmer called system monitoring software keeps track of, logs, and examines all levels of the IT ecosystem's system resources, from operating systems and electronics to the underlying system applications and services, to running user software. At the top Today, 80% of all child abuse cases reported around the globe. The remaining 26% were boys, with 74% being girls. In this world, a daughter goes missing every 40 seconds. Children If one's children's future is the foundation of one's nation, is affected, it affects overall development Nation. Due to abuses, children's emotional and mental stability is affected Destroying and future endeavors. The things that happen to these defenseless kids are not their fault. Parents are therefore accountable for raising their own children. But, since their children's future and career are not considered, parents are compelled to want for money. So, it is challenging to understand them. There are kids everywhere. We offer a setting in our system where this issue is able to be resolved effectively. It makes it simple for parents to follow their kids in real time as if they were close to them and they focus on their own business without any manual intervention. Ratio studies are statistical analyses of data from appraisals and property valuations. Nearly all states utilise them to produce quantitative measure of the proportion of current market price about which individually estimated taxable property is appraised as well as to offer assessment performance indicators. Evaluation parameters taken as Temperature sensor, Pulse sensor, GPS, GSM and Web camera. The Cronbach's Alpha Reliability result. The overall Cronbach's Alpha value for the model is .658 which indicates 66% reliability. From the literature review, the above 50% Cronbach's Alpha value model can be considered for analysis. Software Engineering Defect Prediction the Cronbach's Alpha Reliability result. The overall Cronbach's Alpha value for the model is .658 which indicates 66% reliability. From the literature review, the above 50% Cronbach's Alpha value model can be considered for analysis.

Implementation of Sibi Sign Language Realtime Detection Program (Case Studi At Sekolah Luar Biasa Negeri 1 Tabanan)

Indonesian deaf people utilize SIBI to communicate using spoken words, gestures, facial expressions, and body language. SIBI, certified for Special Schools (SLB), helps deaf pupils communicate. This project implements SIBI (Indonesian Sign Language System) a real-time detection algorithm at Sekolah Luar Biasa Negeri 1 Tabanan using image processing and YoloV8 ultralytics deep learning. The program trains a sign language gesture detection model on Google Colab's GPU. The SIBI sign language images were used to train a YoloV8 object detection model. The camera captures movements, which the YoloV8 algorithm trained on SIBI gesture data processes. It can recognize gestures in real time and generate text to non-sign language users. The dataset has 107 class vocabulary and 7 class affix prefixes for complete gesture recognition. Shirt color, room brightness, and webcam quality affect detection rates. Optimal detection accuracy is 87.74% and subpar 58.02%. Despite these limitations, the strategy helps deaf students communicate more effectively with non-sign language speakers. This program improves inclusivity and communication in schools, making learning easier for hearing-impaired pupils. This work provides a reliable and quick sign language identification system to help deaf educators and caregivers with daily interactions and education.

Finite MX/M/C Queue with State Dependent Service, Two-Class Arrivals and Impatient Customers

Objectives: 1. To compute the transition probabilities for the queueing system to be in different states. 2. To calculate the average system length and mean waiting time under the specified parameters. 3. To investigate and test the impact of system characteristics on the anticipated system length as well as mean waiting times. Method: MATLAB software is used for the Runge-Kutta method to calculate probabilities and system constants. Findings: An increase in Type-I arrival rate λ1 and Type-II arrival rate λ2 will cause both mean waiting times and queue length to increase, and they are reduced with an increase in service rates µ1 and µ2 of Type-I and Type-II classes. A rise in parameters such as special service rate µ3, probability of balking and reneging will result in a reduction in both mean waiting times and queue length. Novelty: This article covers a batch arrival finite Markovian queueing system with multi-server support and consumer impatience. In real time, all arrivals need not enter the queue at the same arrival rates, as it is based on the needs of the customer. In view of this, we have assumed two types of customers along with two different rates of arrival and state-dependent service. We did our work in transient mode, as time can influence the waiting lines. Also determined the system's performance indices and demonstrated the impact of the input parameters on the system's constants. Keywords: Batch Arrival, Customer Impatience, Two Class Customers, State Dependent Service, Multi Server Facility

Pineapple Detection with YOLOv7-Tiny Network Model Improved via Pruning and a Lightweight Backbone Sub-Network

High-complexity network models are challenging to execute on agricultural robots with limited computing capabilities in a large-scale pineapple planting environment in real time. Traditional module replacement often struggles to reduce model complexity while maintaining stable network accuracy effectively. This paper investigates a pineapple detection framework with a YOLOv7-tiny model improved via pruning and a lightweight backbone sub-network (the RGDP-YOLOv7-tiny model). The ReXNet network is designed to significantly reduce the number of parameters in the YOLOv7-tiny backbone network layer during the group-level pruning process. Meanwhile, to enhance the efficacy of the lightweight network, a GSConv network has been developed and integrated into the neck network, to further diminish the number of parameters. In addition, the detection network incorporates a decoupled head network aimed at separating the tasks of classification and localization, which can enhance the model’s convergence speed. The experimental results indicate that the network before pruning optimization achieved an improvement of 3.0% and 2.2%, in terms of mean average precision and F1 score, respectively. After pruning optimization, the RGDP-YOLOv7-tiny network was compressed to just 2.27 M in parameter count, 4.5 × 109 in computational complexity, and 5.0MB in model size, which were 37.8%, 34.1%, and 40.7% of the original YOLOv7-tiny network, respectively. Concurrently, the mean average precision and F1 score reached 87.9% and 87.4%, respectively, with increases of 0.8% and 1.3%. Ultimately, the model’s generalization performance was validated through heatmap visualization experiments. Overall, the proposed pineapple object detection framework can effectively enhance detection accuracy. In a large-scale fruit cultivation environment, especially under the constraints of hardware limitations and limited computational power in the real-time detection processes of agricultural robots, it facilitates the practical application of artificial intelligence algorithms in agricultural engineering.

Line-Field Optical Coherence Tomography: Usefulness in the Non-Invasive Differential Diagnosis of Congenital Alopecia of Infancy

Introduction: Soon after birth, the clinical differential diagnosis between sebaceous of Jadassohn (NSJ), congenital triangular alopecia (CTA) and aplasia cutis congenita (ACC) may be challenging. A certain overlap of standard dermoscopic features can occur, especially in atypical cases, depending on scalp skin morphology and maturation age. The recently developed line-field confocal optical coherence tomography (LC-OCT) can provide morphological skin details with cellular resolution trough a rapid non-invasive examination. Objectives: To assess the LC-OCT features of 6 cases of congenital alopecia of different aetiologies, with both typical and atypical clinical appearance. Methods: A non-invasive imaging examination combining standard dermoscopy, high-resolution videodermoscopy (HRVD) and LC-OCT was realized in 7 babies presenting for congenital alopecia with overlapping features, aged between 5 months and 5 years. Results: Based on the specific LC-OCT features, and supported by HRVD features, a diagnosis of nevus sebaceous of Jadassohn (NSJ), congenital triangular alopecia (CTA) and aplasia cutis congenita (ACC) were made in 4, 2 and 1 case, respectively. Conclusions: The combined LC-OCT plus HRVD non-invasive imaging bring the advantage to have a real time diagnosis, to set the proper management and allows to avoid a skin biopsy in the perinatal age/first years of life at delicate skin site.

Portfolio Optimization Strategies for the Common Investor

Abstract: Financial Analytics is a key field to making investment decisions and has been developed only recently. It involves analytical thinking, computational knowledge and creative thinking about investment strategies. There are many ways to invest money. Some people invest them in Gold, some in real estate, some create a fixed deposit, some in the stock market. Some people diversify their money into different sectors of the market and invest to get an optimal profit by understanding the market. This kind of investment is called portfolio management. Unlike gambling, understanding of the portfolio and its management can actively minimize the risks associated with investment. In the proposed research we tried to study the different strategies of investing in the different sectors and building a portfolio, with the express aim of creating optimal diversified portfolios. Our methods include optimizing the weights of different investment instruments in different sectors, with implementations carried out in Python and using time-series Python libraries. We have focussed on three different investment strategies in particular: (1) Genetic algorithms help us to divide the portfolio (chromosome) into the genes (sectors) and understand it in a better way. Exploiting the existing algorithms for optimizing fitness functions in genetic algorithms, we have implemented one strategy for optimizing portfolios; (2) Efficient frontier analysis from Markowitz theory helps in investing and creating our own portfolio. We developed some of the strategies that help in displaying portfolio allocation and efficient frontier graphs; (3) We have also implemented a new criterion in vogue in the financial markets today, the “Kelly Criterion” preferred by many investors all over the world. We used real time financial stock data to validate and test our functions. All the strategies developed are modular and we also show a proof-of-concept web interface for one of the developed strategies. It is hoped that our work enables investors in making better decisions about where to park their money and converting their wealth into high-yield portfolios.

Development and application of a simplified biophysical model to study deltaic and coastal ecosystems

Many coastal regions around the globe experience land loss due to high relative sea level rise rates, declining sediment supply, and a host of other anthropogenic factors. To evaluate possible restoration strategies, we developed a computationally efficient Simplified Biophysical Model (SBM). The SBM includes hydrodynamic, morphodynamic, and marsh inundation components. The hydrodynamic and mineral sediment processes of the SBM are based on open source Delft3D models. The marsh inundation MATLAB module is a simplified vegetation response to salinity and inundation used to estimate annual organic accretion rates. Organic accretion is added annually to the morphodynamic calculations of mineral sediment. The typical run time for the SBM for an area of 4500 km2 is ∼0.8, 2.5, and 4.7 days real time for one, three, and five decade simulations, respectively which is considered a computationally efficient for modeling decadal landscape evolution. The utility of the SBM was demonstrated through an application to assess the performance of a sediment diversion in the Barataria Basin in Louisiana, USA. The model results demonstrate the importance of incorporating the impact of salinity and inundation effects on the resilience of marshes subjected to high rates of relative sea level rise. Further, the sediment reduction analysis confirms the critical impact of the Mississippi River sediment decline on potential land building from sediment diversions where 1.5%, and 3% annual declining rates show a reduction in net land gain due to Mid-Barataria Sediment Diversion operation of 30% and 50 %, respectively. This highlights the importance of considering the Mississippi River sediment supply decline in restoration projects’ planning and specifically, sediment diversions.

Machine Learning-Based Simple and Fast Approach for the Real-Time Characterization of Laser-Induced Plasma

Abstract We report a simple approach to estimate the fast and real-time pulse-to-pulse prediction of plasma parameters, specifically electron temperature and electron number density, using an Artificial Neural network (ANN) in combination with Laser-induced breakdown spectroscopy (LIBS). In a variety of spectroscopic applications, it is essential to have real-time observation of plasma parameters. However, direct measurement of these parameters is challenging and requires complex and time-consuming calculations. Artificial Neural Network (ANN) can be used to model the relation between spectral features from recorded LIBS emission spectra and plasma parameters. The ANN is trained on a suitable preprocessed spectroscopic dataset with corresponding plasma parameters to predict electron temperature and electron number density. The accuracy of Artificial Neural Network (ANN) in predicting the plasma parameters is evaluated, and results are validated with existing conventional methods of calculating plasma parameters, namely the Boltzmann Plot Method for plasma temperature and the Stark Broadening Method for electron number density. The present results show that ANN is an effective method in accurately predicting the plasma parameters directly from the spectral features. The ability to fine-tune plasma in real time enhances control and accuracy in Pulsed Laser Deposition (PLD) process and other plasma coating techniques.

Review on a real time Green Enviro Solar Systems for On-grid and Off-grid Applications with AI and IoT Technologies

Abstract: Solar photovoltaic (PV) plants must be constantly protected and supervised to keep them safe and reliable during their working lifetime. Incorporating smart technological equipment including artificial intelligence (AI) and internet of things (IoT) for remote sensing, fault detection and diagnosing PV plants can help these plants achieve peak efficiency. While renewable energy appears to be thriving globally, its intermittent nature creates a need to develop solutions for ensuring grid stability.The output of solar panels is influenced by a variety of environmental factors such as the amount of solar radiation, proportion of shadow, weather conditions and so on. Constantly monitoring these variables, particularly for off-grid and remote solar energy plants is a tedious task.

Prototype of Household Water Quality Monitoring System by Utilizing Internet of Things

This research aims to develop a household water quality monitoring system using the Internet of Things (IoT) by monitoring water quality parameters such as pH, Total Dissolved Solids (TDS), turbidity, and temperature in real time. The prototyping method in the development of this system allows continuous interaction between developers and users to meet the needs of the system. Data is collected through literature studies and observations to support system development. The results of this study are that every 1 minute, the sensor data is sent to the server, and then the sensor data will be displayed on the web platform; if the sensor data value is not at the predetermined value limit according to Permenkes number 2 of 2023, the displayed value will be red. The data collected by the sensor is sent to the server built with Node.js and stored in the PostgreSQL database. The monitoring results will also be displayed on a 20x4 I2C LCD. The system successfully monitors water quality parameters in real time, with results displayed on a web platform that is easily accessible to users. The implementation of IoT technology in this system provides an innovative solution for increasing public awareness of the importance of clean water quality. With this system, it is expected to create a healthier and safer household environment for families.

Enzyme-Free In-Situ Electrochemical Measurement Using a Porous Membrane Electrode for Glucose Transport into Cell Spheroids.

Microphysiological systems have attracted attention because of their use in drug screening. However, it is challenging to measure cell functions in real time using a device. In this study, we developed a cell culture device using a porous membrane electrode for in situ electrochemical glucose measurements for cell analysis. First, a porous membrane electrode was fabricated and electrochemically evaluated for enzyme-free glucose measurement. Subsequently, the glucose uptake of MCF-7 spheroids was evaluated using living spheroids, fixed spheroids, supernatants, and glucose transporter inhibitor-treated spheroids. Conventionally, the direct optical measurement of glucose uptake requires fluorescence-labeled glucose derivatives. In addition, the glucose uptake can be evaluated by measuring the glucose concentration in the medium by optical or electrochemical measurements. However, glucose needs to be consumed in the entire cell culture medium, which needs a long culture time. In contrast, our system can measure glucose in approximately 5 min without any labels because of in situ electrochemical measurements. This system can be used for in situ measurements in in vitro cell culture systems, including organ-on-a-chip for drug screening.

Vehicle Classification Algorithm Based on Improved Vision Transformer

Vehicle classification technology is one of the foundations in the field of automatic driving. With the development of deep learning technology, visual transformer structures based on attention mechanisms can represent global information quickly and effectively. However, due to direct image segmentation, local feature details and information will be lost. To solve this problem, we propose an improved vision transformer vehicle classification network (IND-ViT). Specifically, we first design a CNN-In D branch module to extract local features before image segmentation to make up for the loss of detail information in the vision transformer. Then, in order to solve the problem of misdetection caused by the large similarity of some vehicles, we propose a sparse attention module, which can screen out the discernible regions in the image and further improve the detailed feature representation ability of the model. Finally, this paper uses the contrast loss function to further increase the intra-class consistency and inter-class difference of classification features and improve the accuracy of vehicle classification recognition. Experimental results show that the accuracy of the proposed model on the datasets of vehicle classification BIT-Vehicles, CIFAR-10, Oxford Flower-102, and Caltech-101 is higher than that of the original vision transformer model. Respectively, it increased by 1.3%, 1.21%, 7.54%, and 3.60%; at the same time, it also met a certain real-time requirement to achieve a balance of accuracy and real time.

Integrating machine learning and blockchain: Conceptual frameworks for real-time fraud detection and prevention

Integrating machine learning (ML) and blockchain technologies presents a groundbreaking approach to real-time fraud detection and prevention, addressing the growing complexity and sophistication of financial fraud schemes. This integration leverages the strengths of both technologies: the predictive power of ML algorithms and the transparency, security, and immutability of blockchain. Machine learning algorithms are proficient at analyzing large datasets, detecting patterns, and identifying anomalies that signify potential fraud. By employing supervised learning models such as logistic regression, decision trees, and neural networks, financial institutions can classify transactions and predict fraudulent activities with high accuracy. Unsupervised learning techniques, such as clustering and anomaly detection, are instrumental in discovering new fraud patterns without the need for labeled data, thus enhancing the detection of novel fraudulent behaviors. Blockchain technology, on the other hand, provides a decentralized and tamper-proof ledger that ensures data integrity and traceability. Transactions recorded on a blockchain are immutable and transparent, allowing for real-time monitoring and auditing. Smart contracts, self-executing contracts with the terms directly written into code, can be programmed to trigger alerts or actions when suspicious transactions are detected, further automating the fraud prevention process. The conceptual framework for integrating ML and blockchain involves several key components. First, data from financial transactions are continuously collected and stored on a blockchain, ensuring transparency and security. ML algorithms analyze this data in real time, identifying suspicious patterns and flagging potential fraud. When a potential fraud is detected, a smart contract is executed, which can instantly block the transaction, alert the relevant authorities, or initiate further verification processes. This integrated approach addresses several challenges in traditional fraud detection systems. The decentralized nature of blockchain eliminates single points of failure and reduces the risk of data tampering. The transparency of blockchain enhances the trustworthiness of the detection process, while the predictive capabilities of ML provide high accuracy and adaptability to new fraud tactics. Additionally, the real-time processing capabilities of both technologies ensure prompt detection and prevention of fraudulent activities. In conclusion, the integration of machine learning and blockchain offers a robust framework for real-time fraud detection and prevention. This synergy not only enhances the security and reliability of financial transactions but also paves the way for more advanced and automated compliance systems, ultimately strengthening the financial ecosystem against fraudulent threats.

IoT-enabled healthcare transformation leveraging deep learning for advanced patient monitoring and diagnosis

AbstractDeep learning and the Internet of Things (IoT) are revolutionizing the healthcare industry. This study explores the potential commercial transformation resulting from IoT-enabled healthcare systems that use deep learning for patient monitoring and diagnosis. Wearables, smart sensors, and internet-connected medical devices allow doctors to monitor patients' vital signs, activities, and physiological traits in real time. However, these devices generate vast and complex data, making analysis and diagnosis challenging. Deep learning models are well-suited to analyze this growing volume of medical data. Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks can automatically recognize complex patterns and relationships in sensor data, electronic health records, and patient-reported information. This capability aids clinical professionals in diagnosing illnesses, identifying warning signs, and tailoring treatments. This paper describes a Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) -based IoT-enabled healthcare system that performs feature extraction, classification, prediction, and data preparation. Additionally, it addresses interpretability issues, privacy concerns, and resource limitations of deep learning models in real-time healthcare settings. The study demonstrates the effectiveness of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) -powered IoT-based healthcare solutions, such as real-time patient monitoring, disease detection, risk prediction, and therapy optimization. These techniques can improve the quality, cost, and outcomes of healthcare. Combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) with IoT can significantly enhance healthcare by improving disease detection, personalized treatment, and patient monitoring through connected devices and powerful analytics.

Smart Waste Management System for Smart Cities

A smart waste management system represents aninnovative approach of smart bin by integrating IoT intelligence. The system incorporates sensor-based devices deployed within smart bin to monitor in real time. This bin allows efficient route planning, reducing operational costs and emissions and timely waste collection improves hygiene, reducelittering and enhances overall quality of life. Smart bin collects the data from the sensors, which is then store into the firebase, through the app municipal authorities and the public receives the notifications regarding the bin fill status, safety alerts, and can track location of the bin. It utilizes solar power for sustainable power supply and camera for live streaming. The system provides a web interface to the municipal authority so that they can monitor and clean the garbage bin. The system enhances the public engagement, and contributes to a cleaner and greener environment.

MODELS OF REMOTE IDENTIFICATION OF PARAMETERS OF DYNAMIC OBJECTS USING DETECTION TRANSFORMERS AND OPTICAL FLOW

The tasks of remote identification of parameters of dynamic objects are important for various fields, including computer vision, robotics, autonomous vehicles, video surveillance systems, and many others. Traditional methods of solving these problems face the problems of insufficient accuracy and efficiency of determining dynamic parameters in conditions of rapidly changing environments and complex dynamic scenarios. Modern methods of identifying parameters of dynamic objects using technologies of detection transformers and optical flow are considered. Transformer detection is one of the newest approaches in computer vision that uses transformer architecture for object detection tasks. This transformer integrates the object detection and boundary detection processes into a single end-to-end model, which greatly improves the accuracy and speed of processing. The use of transformers allows the model to effectively process information from the entire image at the same time, which contributes to better recognition of objects even in difficult conditions. Optical flow is a motion analysis method that determines the speed and direction of pixel movement between successive video frames. This method allows obtaining detailed information about the dynamics of the scene, which is critical for accurate tracking and identification of parameters of moving objects. The integration of detection transformers and optical flow is proposed to increase the accuracy of identification of parameters of dynamic objects. The combination of these two methods allows you to use the advantages of both approaches: high accuracy of object detection and detailed information about their movement. The conducted experiments show that the proposed model significantly outperforms traditional methods both in the accuracy of determining the parameters of objects and in the speed of data processing. The key results of the study indicate that the integration of detection transformers and optical flow provides reliable and fast determination of parameters of moving objects in real time, which can be applied in various practical scenarios. The conducted research also showed the potential for further improvement of data processing methods and their application in complex dynamic environments. The obtained results open new perspectives for the development of intelligent monitoring and control systems capable of adapting to rapidly changing environmental conditions, increasing the efficiency and safety of their work.

OffloaD: Detection Failure-based Scheduler for Offloading Object Detection

The current times ask for resource-constrained devices such as drones, light mobile robots, XR glasses, or mobile phones to perform object detection efficiently and in real time. However, when executed on the device, object detection fails to achieve the accuracy requirements. In addition, depending on a powerful edge to offload computation is time-consuming as it requires the transmission of a large amount of data and limits the usage in degraded network conditions. In this paper, we propose OffloaD, a novel offloading scheduler based on the detection of failures for object detection. The method addresses the problem of offloading between a resource-constrained device and an edge by leveraging the accuracy of the object detection model at the device and the network conditions. Accuracy is considered through a detection failure metric that consists of introspective, golden and latency scores. Our main contribution is the offloading scheduler based on the detection of failures of the object detector, which reduces unnecessary data transmission without accuracy degradation. Our method demonstrates that offloading to expensive detectors will not always increase performance and enables detection results and failure estimations even if the network is degraded or unavailable. We implement OffloaD in a physical testbed and perform extensive comparisons against several baselines. The experiments suggest that our approach improves the overall precision-latency performance by reducing the end-to-end latency between 18% and 37% in ideal network conditions and a low latency increase in degraded network conditions while obtaining an accuracy with minimum degradation compared to the baselines. In addition, comparisons to related work methods demonstrate the state-of-the-art performance of OffloaD.

Enhancing Driving Safety through User Experience Evaluation of the C-ITS Mobile Application: A Case Study of the DARS Traffic Plus App in a Driving Simulator Environment

The paper evaluates the DARS Traffic Plus mobile application within a realistic driving simulator environment to assess its impact on driving safety and user experience, particularly focusing on the Cooperative Intelligent Transport Systems (C-ITS). The study is positioned within the broader context of integrating mobile technology in vehicular environments to enhance road safety by informing drivers about potential hazards in real time. A combination of experimental methods was employed, including a standardised user experience questionnaire (meCUE 2.0), measuring quantitative driving parameters and eye-tracking data within a driving simulator, and post-experiment interviews. The results indicate that the mobile application significantly improved drivers’ safety perception, particularly when notifications about hazardous locations were received. Notifications displayed at the top of the mobile screen with auditory cues were deemed most effective. The study concludes that mobile applications like DARS Traffic Plus can play a crucial role in enhancing road safety by effectively communicating hazards to drivers, thereby potentially reducing road accidents and improving overall traffic safety. Screen viewing was kept below the safety threshold, affirming the app’s efficacy in delivering crucial information without distraction. These findings support the integration of C-ITS functionalities into mobile applications as a means to augment older vehicle technologies and extend the safety benefits to a broader user base.