Proposed System Research Articles

Implementation of Solar Based Multipurpose Agriculture Robot using Random Forest Algorithm

In India majority of the people depends only on agriculture. Agriculture is the process of working on the ground, planting seeds and growing the crops. Nowadays only few are involved in farming it is because of the increasing of the cost for the pesticides, seeds and equipment, and also increasing of the man power. The major drawback in agriculture field is due to reducing of the crop yield. It is because of lack of awareness towards soil i.e., which crop should be grown on particular area. All these factors will make the farmers not to do the farming in an efficient way. This paper proposes Solar based multipurpose agriculture robot using Random Forest Algorithm. This system will reduce the manpower and increase the crop yield. This proposed system will do operations like seed sowing, pesticide spraying, solar panel for getting the energy to run the robot etc. The total work should be done with a most emerging technology like Machine learning. In this we are using a random forest algorithm concept for getting an efficient output which will be more helpful to the farmers and output can be displayed with a mobile app so that he/she can see the details of the field in an easier manner

Smart system for water quality monitoring utilizing long-range-based Internet of Things

Water is the most basic need for humans and a source of livelihood for humans. Lack of human awareness to maintain water quality, causing water to become polluted, by both industrial and household waste, impacts on human health and material loss. Thus, it is important to create technology that can monitor water pollution automatically and quickly. This research aims to create a system which utilizes the Internet of Things (IoT) technology that can facilitate quality of water by measuring parameters such as pH and turbidity. The methodology of the system progresses by the usage of a controller which is ATmega328P-AU, pH sensor to measure acidity, turbidity sensor to measure turbidity level, LPWAN LoRa works like a communication of data transmission as well as cloud service, namely Antares, to store data that are sent via Android. Based on the outcomes, the proposed system has achieved a reliable accuracy with percentage error of 99.73% in pH sensor and 99.41% in the turbidity sensor. Also, 2.6 s is the average required time to deliver the results to the cloud service.

New Enhancement Spectrofluorimetric Determination of Anti‐Breast Cancer Drugs Palbociclib by Biogenic‐Synthesized Alumina/NiO Nanocomposite

AbstractThis study is based on green synthesized nanomaterials that increase fluorescence intensity in the study of the anticancer drug palbociclib. The basis for the nanomaterials was the use of alumina/nickel oxide nanocomposite (alumina/NiO NCS) biogenically produced from millet seed extract. The proposed materials improved the spectrofluorimetric method for the study of the breast cancer drug palbociclib (PAL) in its real powder and capsules. The amplification properties of the developed spectrofluorimetric system depend on the unique optical properties of the alumina/NiO NCS. Fluorescence detection was determined at λex 260 and at λem 430 nm. The effect of surfactant (sodium dodecyl sulfate, SDS) was investigated, and the results obtained with the PAL alumina/NiO/SDS showed linearity of 1.0–800 g/mL for PAL samples with a correlation coefficient of r>0.999. The aforementioned fluorescence approach showed lower detection and quantification of 0.03 and 1.0 g/mL, respectively. The proposed system was potentially efficient for the determination of PAL with an excellent percent recovery of 99.50±0.5 %. The effect of some co‐formulated interfering substances such as crystalline cellulose, starch, silicon dioxide magnesium stearate, lactose and gelatine that present in the capsules was investigated and no remarkable difference was found during the assay process of PAL. The method was validated to ensure its suitability and showed high sensitivity and accuracy for the detection of PAL.

Analysis of the Application Potential of English Translation Strategy Based on Wireless Widget Technology in 5G Technology Reports

As technology continues to evolve, the process of English translation has become easier. A technology called widget, which is used in modern research, provides an efficient graphical user interface for the interaction between the user and the application. This paper compares the newly proposed wireless widget system with existing models of English translation strategies for internet of things (IoT), cloud computing (CC), artificial intelligence and edge computing (AI+EC), and pure internet concepts. These models were compared on metrics such as accuracy, search completion rate, and throughput. The results show that the proposed system outperforms the existing models with an accuracy rate of 98%, a search completion rate of 96%, and a maximum throughput of 96%. The model shows high performance advantages and can effectively improve the efficiency of translation work.

A Real-Time Deep Learning-Based Facial Mask Detection System for Preventing the Transmission of Respiratory Viruses

In order to prevent the spread of respiratory viruses like COVID-19 and influenza, an effective protection method is to wear facial masks in densely populated areas. This has led to a growing need for smart services that automatically detect facial masks and replace manual reminding. To address this challenging task and to contribute towards health safety, this paper introduces MedNetV2 system, which is an efficient deep learning-based facial mask detector with a low computational cost. In comparison with existing systems, the main specificities of the proposed system are: (1) the adoption of an effective deep learning-based framework to deal with both the large scale diversity and position variations of masked faces involved in natural scenes, (2) the interaction between face localization and facial mask detection modules to achieve the overall system goal, (3) the lightweight design and the real-time response well suited for real-world scenarios. Extensive experiments on public dataset and real-world video streaming are carried out to validate quantitatively and visually the effectiveness of the proposed system. Promising results, in terms of detection accuracy as well as time response, are achieved when compared it with other state-of-the-art systems.

Development and Implementation of an FPGA-Embedded Multimedia Remote Monitoring System for Information Technology Server Room Management

Conventional data acquisition systems face challenges in achieving high acquisition speeds and rapid storage of large data volumes using microcontrollers. In contrast, field-programmable gate arrays (FPGAs) offer numerous advantages, including high clock frequencies, minimal internal delays, fast operational speeds, abundant internal RAM resources, and simplified control of complex peripheral circuits. This study presents the design of an FPGA-based multimedia remote monitoring system for information technology server rooms. The proposed system utilizes an FPGA as the primary controller and incorporates environmental sensors, electrical energy sensors, carbon monoxide sensors, smoke sensors, and A/D converter modules to monitor multiple locations within the server room. Simultaneously, the FPGA transmits the collected data from each monitoring point via a serial port to an LCD serial screen for display. An alarm is triggered if any environmental anomalies are detected, indicating abnormal statuses. Additionally, the system employs the fast Fourier transform algorithm and butterfly operations to derive voltage and current AC quantities, while utilizing relative temperature differences to identify equipment faults within the server room. The system is evaluated in terms of functionality, user-friendliness, and reliability. Experimental results demonstrate that all performance measures align with expectations, fulfilling the initial design objectives and highlighting the potential applicability and relevance of FPGA technology in the monitoring field. In addition, a comparison between FPGA and traditional microcontroller systems is performed, showcasing the superior processing speed and performance of the FPGA-based system. This comparative analysis further validates the advantages of using FPGA technology in high-speed data acquisition and monitoring applications.

UFace

Facial expression recognition (FER) is a crucial task for human-computer interaction and a multitude of multimedia applications that typically call for friendly, unobtrusive, ubiquitous, and even long-term monitoring. Achieving such a FER system meeting these multi-requirements faces critical challenges, mainly including the tiny irregular non-periodic deformation of emotion movements, high variability in facial positions and severe self-interference caused by users' own other behavior. In this work, we present UFace, a long-term, unobtrusive and reliable FER system for daily life using acoustic signals generated by a portable smartphone. We design an innovative network model with dual-stream input based on the attention mechanism, which can leverage distance-time profile features from various viewpoints to extract fine-grained emotion-related signal changes, thus enabling accurate identification of many kinds of expressions. Meanwhile, we propose effective mechanisms to deal with a series of interference issues during actual use. We implement UFace prototype with a daily-used smartphone and conduct extensive experiments in various real-world environments. The results demonstrate that UFace can successfully recognize 7 typical facial expressions with an average accuracy of 87.8% across 20 participants. Besides, the evaluation of different distances, angles, and interferences proves the great potential of the proposed system to be employed in practical scenarios.

Exploring the Conceptual Framework of Mobile Learning Technology

The idea of introducing new technologies to education is not new, and as a result, research on this topic has been carried out for years. However, the rate of introduction of new technologies in higher education in Uzbekistan is very slow. The purpose of this article is to propose a conceptual framework for the development of mobile learning, in particular, the introduction of mobile applications into the educational process in primary education. This goal is achieved by analyzing the existing measures and the existing foundations of mobile education, which are used to improve the quality of education through the active use of information and communication technologies. The proposed system includes three main elements: applied computing, information systems, and theory of computation. It is important that each of them is done correctly and that there is continuity between them.

Performance evaluation and optimization of a multigeneration hybrid system for power, hydrogen, cooling, and freshwater production from renewable sources

Designing a hybrid system emerges as a highly efficient strategy for augmenting the performance of renewable-based power plants, owing to their adaptability in integrating diverse subsystems. In this study, a new multigeneration system is introduced, showcasing its capability to concurrently generate power, hydrogen, cooling, and freshwater through a synergy of methanol-steam reforming, modified absorption refrigeration cycles, humidification-dehumidification, and multi-effect desalination. The operational foundation of the system is rooted in the harmonious integration of solar and geothermal energy sources. To comprehensively evaluate the proposed system's performance, a combination of exergy and economic analyses is employed. This research studies the impacts of varying key parameters on the system's operation. Notably, the water-to-methanol ratio is identified as the most influential variable affecting reformer performance, underscoring the pivotal role of reforming pressure. The estimated total fixed cost of the system stands at $4.039 million, with a calculated payback period of 6 years and a net present value of $3.454 million. Furthermore, in its optimized state, the system achieves an impressive exergy efficiency of 49.80 % and a unit product cost of $32.38/GJ. These findings unequivocally validate the feasibility and economic viability of the proposed hybrid renewable-based power generation system. In conclusion, the amalgamation of solar and geothermal energy, coupled with meticulous parameter optimization, substantiates the potential of this innovative system to serve as a sustainable and economically viable solution for power generation.

FruitScan Pro : Fruit Disease and Quality Detection Application

This research paper proposes a revolutionary approach to agriculture by combining the latest image processing technologies with machine learning. The agricultural industry, which is vital for India’s economy, needs efficient methods for monitoring fruit health. Manual monitoring is time consuming and often inadequate. In this paper, we propose a smart farming approach using image processing techniques. This approach improves image quality, segmenting images, extracting features, and classifying diseases and their cures. Fruit diseases are a major economic challenge around the world. Therefore, it is necessary to identify them quickly. Our proposed system offers fast and automatic disease detection. Preprocessing, extracting features, classifying, training/testing. The results prove its effectiveness for accurate disease detection and solutions. As the demand for agricultural productivity increases, this method will be a valuable tool for increasing fruit production with minimum human intervention. Image processing for effective implementation. Key Words: Fruit Disease, Solution, Image Preprocessing, Feature Extraction, Classification.

Bi-level Analysis of Computed Tomography Images of Malignant Pleural Mesothelioma: Deep Learning-Based Classification and Subsequent Three-Dimensional Reconstruction

Abstract A bi-level analysis of computed tomography (CT) images of malignant pleural mesothelioma (MPM) is presented in this paper, starting with a deep learning-based system for classification, followed by a three-dimensional (3D) reconstruction method. MPM is a highly aggressive cancer caused by asbestos exposure, and accurate diagnosis and determination of the tumor’s volume are crucial for effective treatment. The proposed system employs a bi-level approach, utilizing machine learning and deep learning techniques to classify CT lung images and subsequently calculate the tumor’s volume. The study addresses challenges related to deep neural networks, such as the requirement for large and diverse datasets, hyperparameter optimization, and potential data bias. To evaluate performance, two convolutional neural network (CNN) architectures, Inception-v3 and ResNet-50, were compared in terms of their features and performance. Based on CT images, the second stage incorporates 3D volume reconstruction. The process is carried out by cropping, registering, filtering, and segmenting images. This study demonstrated the efficacy of the developed system by combining CNN optimizations with 3D image reconstruction. It is intended to improve the accuracy of MPM diagnosis and to assist in the determination of chemotherapy doses, both of which may result in improved outcomes for patients.

Design and Implementation of Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images

Functional magnetic resonance imaging (fMRI) is a neuroimaging technique that uses MRI technology to quantify brain activity by detecting changes in blood flow and applying the Blood-oxygen-level dependent (BOLD) contrast. Current neuroscience research suggests a modular brain structure. To understand the complex patterns of interaction between brain areas. The proposed system makes use of the CNN algorithm, which is an efficient method for dividing segmentation. A brain area is defined as a collection of subjects who exhibit a similar pattern of interaction between their brain areas. A thorough experimental examination utilizing benchmark data demonstrates that our technique is both effective and efficient. Two real-world fMRI studies found that the rifleman technique has the potential to improve comprehension of both normal brain activity and the abnormalities associated with psychiatric diseases. That is, mental diseases are typically defined by How an individual feels, acts, thinks, or perceives events. This is typically linked to specific areas or functions of the brain or the rest of the nervous system. This is unacceptable for a variety of time series sectors. As a result, we provide the statistical region merging approach, which is applied to picture segmentation. The method evaluates the values inside a regional range and combines them using the merging criteria, resulting in a smaller list. . As a result, we gather additional information and compare the findings to those already in the database. As a result, we decide if a person is normal or aberrant, as well as whether they are unwell.

A multi-platform Guidance, Navigation and Control system for the autosub family of Autonomous Underwater Vehicles

Over the last decade, there has been a proliferation of marine robots such as Unmanned Surface Vehicles (USVs), Autonomous Underwater Vehicles (AUVs) and underwater gliders. The variety of vehicles’ capabilities and styles has led to numerous strategies to address the Guidance, Navigation and Control (GNC) tasks. In particular, a recent trend in the advancements of GNC systems is to develop modular, multi-platform solutions. This paper illustrates the approach followed in the development of the GNC system for the different types of AUV of the Autosub family. The presented system is decomposed into several interconnected components, each implemented as a standalone software module. Each component is highly-reconfigurable and easily extensible, and the interactions between the modules rely on generic-purpose interfaces. Such a design enables the execution of a broad range of autonomous missions, as well as the integration and deployment of the system on marine platforms with very diverse characteristics, not limited to the vehicles of the Autosub class. Experimental results obtained in real-time during in water campaigns are provided to demonstrate the effectiveness of the proposed system and its versatility on a wide range of platforms and missions.

FabricFolding: learning efficient fabric folding without expert demonstrations

AbstractAutonomous fabric manipulation is a challenging task due to complex dynamics and potential self-occlusion during fabric handling. An intuitive method of fabric-folding manipulation first involves obtaining a smooth and unfolded fabric configuration before the folding process begins. However, the combination of quasi-static actions like pick & place and dynamic action like fling proves inadequate in effectively unfolding long-sleeved T-shirts with sleeves mostly tucked inside the garment. To address this limitation, this paper introduces an enhanced quasi-static action called pick & drag, specifically designed to handle this type of fabric configuration. Additionally, an efficient dual-arm manipulation system is designed in this paper, which combines quasi-static (including pick & place and pick & drag) and dynamic fling actions to flexibly manipulate fabrics into unfolded and smooth configurations. Subsequently, once it is confirmed that the fabric is sufficiently unfolded and all fabric keypoints are detected, the keypoint-based heuristic folding algorithm is employed for the fabric-folding process. To address the scarcity of publicly available keypoint detection datasets for real fabric, we gathered images of various fabric configurations and types in real scenes to create a comprehensive keypoint dataset for fabric folding. This dataset aims to enhance the success rate of keypoint detection. Moreover, we evaluate the effectiveness of our proposed system in real-world settings, where it consistently and reliably unfolds and folds various types of fabrics, including challenging situations such as long-sleeved T-shirts with most parts of sleeves tucked inside the garment. Specifically, our method achieves a coverage rate of 0.822 and a success rate of 0.88 for long-sleeved T-shirts folding. Supplemental materials and dataset are available on our project webpage at https://sites.google.com/view/fabricfolding.

Social Media Monitoring Of Airbnb Reviews Using AI: A Sentiment Analysis Approach For Immigrant Perspectives In The UK

This paper presents a novel approach for monitoring social media content related to Airbnb reviews, explicitly focusing on the sentiments expressed by immigrants in the United Kingdom. The proposed system, a Quick Search System, leverages machine learning techniques to perform sentiment analysis on many Airbnb reviews. The system aims to provide timely and insightful information about the experiences and sentiments of immigrants in the UK, as reflected in their Airbnb reviews. By employing state-of-the-art machine learning algorithms, the system enables efficient and accurate sentiment classification, allowing for the identification of key themes and sentiments expressed by immigrant users. The study demonstrates the potential of this approach in gaining a deeper understanding of immigrant perspectives within the context of peer-to-peer accommodation, and its implications for social media monitoring and customer satisfaction management.This present study has conducted a critical analysis utilizing efficient feature extraction techniques, including N-grams and TF-IDF, to optimize identifying positive, neutral, and negative feedback. Furthermore, five different models were utilized, and the training and testing processes were accompanied by parameter tuning. Ultimately, the study concluded that the Random Forest (RF) classifier performed exceptionally well, achieving a 95% accuracy rate.

Internet of Bio-NanoThings privacy: securing a multi compartmental targeted cancer drug delivery scheme

AbstractThe Internet of Bio-Nano Things concept (IoBNT) emerged from the need to establish connections between biological nanomachines, the intra-body nanonetwork, and the cyber internet to facilitate information exchange. While extensive research has concentrated on optimizing communication efficiency among nanodevices within networks, challenges such as IoBNT security and the interface linking nanonetwork to the internet have remained unaddressed. Consequently, this study introduces a privacy scheme designed to operate atop the Physical Cyber Interface (pHCI) within the IoBNT framework. Our proposed chaotic system derives its foundation from the command signals issued by medical personnel to pHCI devices implanted within the human body. It employs a concealed version of features generated through a Modified Quadratic Map (MQM) to enhance the privacy of patient information and to ensure a precise dosage release. Additionally, our scheme incorporates Binary Phase Shifting Key (BPSK) modulation through the incorporation of a carrier wave, along with feature extraction with zero-crossing rates. This privacy scheme significantly amplifies the key space, thereby guaranteeing an accurate right dose release with the protection of patient privacy. To assess the performance of our proposed scheme, we evaluate its operation on top of the pHCI device using various performance metrics. Subsequently, we study its performance by employing multi-compartmental models in both the forward and reverse pHCI directions of the IoBNT paradigm. The results from our simulation model clearly illustrate that the IoBNT-based privacy scheme has potential to enhance the delivery of therapeutic drugs to target cells while effectively addressing privacy concerns. An evaluation of performance metrics for two binary codes (thermal and light) reveals sensitivity and specificity rates of 95.333% and 95%, 100%, and 100%, respectively. Furthermore, the performance of our proposed privacy scheme, as measured by EER, accuracy, NPV, and PPV, has proven to be highly satisfactory. Hence, our proposed scheme makes significant role in enhancing the security of the physical cyber interface device while remaining cost-effective, and ensuring the safety of patients' life and confidentiality.

Fast DC-link voltage control based on power flow management using linear ADRC combined with hybrid salp particle swarm algorithm for PV/wind energy conversion system

Hybrid Energy Systems (HESs) are offering several advantages over conventional generation systems because of the rising need for Renewable Energy Resources (RERs) and its high capability and flexibility of achieving the desired performance for both urban and rural areas. The main goals of HESs are to improve the system performance by increasing its flexibility, reliability and reducing generation instability. In the current work, a hybrid Photovoltaic/Wind Energy Conversion System (PV/WECS) is proposed to supply and support the utility grid. The generated power from the system is greatly responding to the change in both the wind speed and PV irradiance level, and as a result impacting the DC-Bus voltage of the system converters. Therefore, precise, fast and optimum controllers are required for the PV/WECS system to ensure the extraction and generation of stable maximum power during weather variations along with maintaining the DC-Link voltage at the desired value. Further, this study proposes a novel Linear Active Disturbance Rejection Control (LADRC) combined with a hybrid Salp Particle Swarm Algorithm (SPSA) to a grid-connected PV/WECS for extracting the Global Maximum Power (GMP), regulating the DC-Link voltage, and enhancing the active and reactive powers control. The capability of the proposed SPSA-LADRC based-MPPT approach is examined according to the tracking time, accuracy and response to internal and external disturbances and then comparing its performance with other techniques such as SPSA-SMC, SPSA-PID and SPSA-PI controllers respectively. The findings revealed that the LADRC can track and keep the DC-Link voltage as the desired reference value,1500 V, respond much faster than the PI, SMC, and PID controllers as well as keep the THD of the injected current into the grid at a low level of 2.25%. Finally, the performance of the proposed system is experimentally verified using Hardware-In-Loop (HIL). The obtained findings verify the capability and accuracy of the system and its great potential in the management of HESs.

Numerical simulation and experimental investigation on Phase Change Materials based energy storage system for cooling the water in process industries towards water conservation and environmental sustainability

Water conservation is one of the significant concerns in the industrial sector in cooling processes. Water is lost predominantly in the cooling towers by evaporation, drift, and blowdown. A new Phase Change Material (PCM) based cooling system is proposed as a replacement for cooling towers to eliminate water losses completely. In the current research, the temperature drop of water known as range in the cooling tower is investigated in the proposed system by varying the process parameters such as water velocity, tube materials and tube lenght. In the present study, OM50, an organic PCM with a melting temperature of 500 C is chosen for experimental and numerical analysis as the normal operating temperature of water in the cooling towers is in this range. Based on the experiment, it is observed that the temperature range of water almost remains constant for the melt fraction values of PCM from about 0.1 to 0.8 and taking about 35 % of the total time required for completely melting the PCM. However the cooling range steeply decreases for the melt fraction variation from 0.8 to 1. Hence the designing of PCM based cooling system will be effective for the melting of the PCM from 0.1 to 0.8 leading to constant cooling range of temperature. The experimental results obtained are also validated by CFD simulation using ANSYS 18.1 and the results obtained are in close agreement with each other. The range obtained in the present research is almost in the same range of cooling tower used in the chiller plant presented in the case study. The proposed system may be able to replace cooling towers towards conservation of water and environmental sustainability.

An All-Terrain Intelligent Autonomous Vehicle with Sensor Fusion Based Navigation Capabilities

An automated defense robot building is planned, that has a laser gun attached, which is utilized for pointing laser rays to destroy the target object. One of the most important things about these robots is that they have the capability to perform missions remotely in the field, without any actual danger to human lives. In the proposed system, a robot is developed that is controlled through Microcontroller. For the aiming purpose and to view the road and the surroundings in which the robot is travelling, wireless cameras are installed. Radio frequency can be used to control the robot. These defense robots used in military are usually employed with the integrated system including gripper, cameras and sensors. This is specially designed for defense robotic system to save human life and protect the country from enemies. In the existing systems, personal computer using ZigBee protocol is used to monitor the robot.

A novel UEBASC‐qZSI‐based BLDC motor drive with modified DTC for EV application

SummaryAt present taking into account the situation of the environment and the growing demand of fossil fuels, electric vehicles (EVs) are a highly promising option for the transportation system. Many manufacturers of electric vehicles employ BLDC motors as the vehicle's driving component. In contrast to the many advantages that BLDC motors offer, one of the major drawbacks of employing these motors is the production of relatively high distortion in electromagnetic torque that is created by commutation. This distortion in torque has an effect on the vehicle's performance. In this proposed work a BLDC motor is powered by a cutting‐edge, highly gained q‐ZSI Ultra‐Extended Boost Active Switch Controlled Based Quasi Z‐source Inverter, or UEBASC‐qZSI. In the aforementioned drive, the torque distortion is minimized with the use of a modified direct torque control approach. With the aid of this UEBASC‐qZSI, the efficiency of EVs can be improved, and the overall safety of the driving cycle is also increased. Any vehicle's efficiency can be enhanced with the help of this UEBASC‐qZSI, and the overall safety of the overall system can also be improved. To validate the efficacy of the system, a Simulink model is developed in the MATLAB platform. Also, a hardware prototype model is developed for experimental authentication. The modeling and investigational findings demonstrate that the proposed system has significantly reduced the torque ripple while obtaining the necessary input dc bus voltage.