Browse Items (16488 total)
Sort by:
-
Ion-imprinted carbon dots: rationally designed fluorescent probes for the detection of selected metal ions from aqueous solutions
Photoluminescence properties of Carbon Dots (CDs) have been leveraged for their use as sensors for a variety of analytes, including inorganic ions, organic molecules, and biomolecules. The selective fluorescence response of CDs to specific analytes is generally not pre-designed. Rationally designed synthesis of CDs with pre-defined selectivity to specific analytes is a less explored avenue. This study presents a novel method for the customized synthesis of CD fluorescent probes and an ion-imprinting-based selective detection of metal ions using these CDs. Poly(sodium 4-styrenesulfonate) [PSS] treated with Cd(ii) ions was used as the precursor for preparing Cd-imprinted CDs, and a modified form of these CDs was used for the sensing of Cd(ii) in aqueous solutions. As synthesized CDs have Cd(ii) ions on their surface, which were subsequently removed through appropriate chemical treatment. This removal results in binding sites of Cd(ii) ions on the CDs. Formation of such binding sites results in alterations of the fluorescence of CDs. Exposure of these particles to analytes containing Cd(ii) ions leads to the re-occupation of the binding sites by the metal ions, resulting in a distinct fluorescence response, which serves as the sensing readout. Effectiveness of this ion-imprinting approach is demonstrated by the selective and sensitive fluorescence response of the CDs towards Cd(ii) ions, with a limit of detection (LOD) of 3.62 nM. This strategy of Cd(ii) detection using ion-imprinted CDs represents a novel effort in CD-based sensors, and this can be extended to the sensing of other cations also. This journal is The Royal Society of Chemistry, 2025 -
Ion-imprinted chitosan-stabilized biogenic silver nanoparticles for the electrochemical detection of arsenic (iii) in water samples
Arsenic is one of the most harmful heavy metals, and needs constant monitoring and control. A susceptible and selective sensor for arsenic (iii) detection in polluted water samples has been developed by using chitosan-stabilized silver nanoparticles (AgNPs). A glassy carbon electrode (GCE) is modified with chitosan-stabilized silver nanoparticles, which provides sufficient sites for interaction with the analyte. The synthesized silver nanoparticles are characterized using different characterization techniques, such as UV-Visible spectroscopy, transmission electron spectroscopy (TEM) and particle size distribution. The particle size and polydispersity index (PDI) value of the chitosan stabilized silver nanoparticles are found to be 6 nm and 0.3, respectively, which enhances the electroactive surface area and hence the sensor sensitivity. The ion imprinting technique is used to improve the selectivity of the sensor. The arsenic sensor is found to be very selective in the presence of other possible interferents like Pb2+, Zn2+, Cu2+, Ag+ and Fe2+. The detection of As(iii) was performed using differential pulse anodic stripping voltammetry (DPASV) and the detection limit was found to be 11.39 pM. The developed sensor is successfully tested for the picomolar level detection of arsenic (iii) in different water samples. 2023 The Royal Society of Chemistry. -
Ionic strength and phase systems influence nanotubular material functionality
We synthesized novel thiacyanine chromonic liquid crystals (CLCs) and structurally characterized using NMR and mass spectrometry. The impact of distinct substitution at the para position of aromatic counter anions, aliphatic counter ion chain length, and varied spacer parity of thiacyanine dyes on CLC formation is investigated. Liquid crystal properties of the synthesized dyes are characterized by polarizing optical microscopy (POM) and X-ray diffraction (XRD) studies. Dyes exhibit nematic (N), lamellar (L?), columnar rectangular (Colr), and columnar oblique (Colob) CLCs at different concentrations in the water. Electronic absorption spectra reveal Scheibe aggregation in all the dyes. Cylicvoltametry studies confirm redox behaviour in TC-1a and TC-5e dyes. Chromonic LCs hybrid nano-materials are synthesized using solgel method. Scanning electron microscopy employed to confirm nano tubular fiber structure of the hybrid nanomaterilals. 2024 Elsevier B.V. -
IoT and AI for Real-Time Customer Behavior Analysis in Digital Banking
Digital transformation has revolutionized the banking industry, ushering in an era of enhanced customer experiences and operational efficiency. The convergence of Internet of Things (IoT) and Artificial Intelligence (AI) technologies has further propelled this evolution by providing real-time insights into customer behavior. This research explores the integration of IoT and AI for real-time customer behavior analysis in the context of digital banking. The proliferation of connected devices, ranging from smart phones to wearables, has generated an unprecedented volume of data. IoT facilitates the collection of diverse data points, such as transaction history, location information, and device interactions, creating a comprehensive digital footprint for each customer. Simultaneously, AI algorithms leverage this wealth of data to analyze, predict, and respond to customer behavior dynamically. In the realm of digital banking, understanding and adapting to customer behavior in real-time is crucial for providing personalized services, preventing fraud, and optimizing operational processes. This research delves into the mechanisms by which IoT sensors and devices, coupled with AI algorithms, enable banks to gain deeper insights into customer behavior patterns. Key components of the proposed system include data acquisition through IoT devices, secure data transmission protocols, and AI-driven analytics engines. In conclusion, this research advocates for the symbiotic relationship between IoT and AI in digital banking to enable real-time customer behavior analysis. 2024 IEEE. -
IOT and deep learning based emotion and hate speech regodnition to examine the person mind state in home/health care /
Patent Number: 202221053302, Applicant: Raghvendra Omprakash Singh.
IoT and deep learning based Emotion and hate speech recognition to examine the person mind state in home/health care ABSTRACT There are several applications for AEE, which stands for automatic emotion recognition. Various industries, including advertising, technology, and human-robot interactions, use the emotional responses of individuals as a signal. This paper analyses all pertinent scientific literature to determine the application of sensors. In these publications, numerous strategies that have already been researched or implemented are discussed. -
IoT and Eye Tracking based system for Cerebral Palsy Diagnosis and Assistive Technology
Children with Dyskinetic Cerebral Palsy often suffer significant challenges in interacting with digital devices due to their impaired motor control which hinder the use of traditional input methods such as keyboard and touch screens. The limitations can be overcome by developing assistive technology such as the IOT based system that the paper proposes. The paper proposes an eye-tracking system that uses web-cameras along with computer vision algorithms to detect gaze direction and blink patterns paired with a handheld IOT console that bridges the interaction between the user and the digital interface. The study also proposes ways to assess and evaluate a patient's motor and cognitive functions. The proposed solution keeps in mind the motor limitations of a DCP patient and aims to enhance the accessibility of digital interfaces, providing children with severe motor impairments a more intuitive and inclusive means of interacting with technology. 2025 IEEE. -
IoT and Supply Chain Management: Enhancing Efficiency and Security through Smart Technologies
By offering real-time visibility, increased efficiency, and better security, the Internet of Things (IoT) is revolutionizing supply chain management, this paper investigates. IoT devices enable seamless connectivity between products, warehouses, and logistics networks, allowing businesses to manage inventory, forecast demand, and prevent theft or fraud. Examining case studies of IoT implementations in supply chains, the article explores how data-driven insights maximize logistics, lower running costs, and lower risk-profile. It also suggests ways to improve IoT security in supply chains and tackles the security issues raised by IoT like data leaks and illegal access. IoT-based Supply Chain Management (SCM) solutions notably increase operational efficiency, cost reduction, security, inventory correctness, responsiveness, data processing time, downtime, energy consumption, scalability, and customer happiness, according the comparison table. IoT solutions save costs by 30% and have 95% efficacy-a 26.67% improvement over conventional systems. With only one documented security event year instead of five in conventional systems, security is also enhanced. Response time to disturbances drops and inventory accuracy rises as well. IoT solutions additionally provide 400% scalability increase. 2025 IEEE. -
IoT and Sustainability Energy Systems: Risk and Opportunity
As IoT (Internet of Things) and smart technologies have developed rapidly, many technological advancements have been made possible. The IoTs main objective is to assist in simplifying processes in a number of different felds, to improve the effciency of technologies and protocols, and ultimately to improve quality of life. Although IoT technologies can beneft the population in numerous ways, their development must be evaluated from an environmental viewpoint to ensure that global resources are used effciently and to prevent negative effects. As previously described, considerable research effort is needed to explore the advantages and disadvantages of IoT technologies. Engineering professionals, industrial experts, and academic researchers successfully interacted at the conference. Several key tracks made up the conference, including smart city, energy and environment, e-health, and engineering modeling. Specifcally, the editorial covered a number of topics including (i) IoT in sustainable energy and environmental management, (ii) smart cities enabled by IoT, (iii) ambient assisted living, and (iv) IoT technologies for transportation and low-carbon products. An important outcome of our introductory analysis has been a greater understanding of both the scientifc developments in IoT applications and the potential ecological consequences associated with increasing IoT applications. The Author(s), under exclusive license to Springer Nature Switzerland AG 2023. -
IOT and UAV Integrated System for Proactive Crop Disease Prediction
The productivity of agriculture is often affected by the crop diseases incurring economic loss and reduced food production. Detecting these crop diseases in the early stages is the need of the hour to mitigate the spread of the disease and manage the crop yield effectively. This paper presents a proactive crop disease prediction system by employing the technologies such as Unmanned Aerial Vehicles (UAVs) and Internet of Things (IoT) to detect and mitigate the crop disease at early stages. The employed UAVs have a high resolution camera to capture the aerial images of crops and leaves. The images captured via UAVs are transferred to a computing environment dynamically for analysis purposes. The analysis is performed on the images to predict the disease of the leaf along with its intensity. After analyzing and predicting the leaf disease and its intensity, the pesticide information is passed to the IoT sensors which are fixed in the fields to spray the recommended pesticide. The system analyzes the captured images by leveraging the machine learning algorithms in the realtime to identify different diseases and predict the spread of the disease. The proposed proactive approach helps the farmers to take preventive actions to identify the crop disease at the early stage and mitigate them in a timely manner to produce a better crop yield. Moreover, the proposed method provides a cost-effective solution for disease prediction and is easily accessible to farmers to enhance crop productivity. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
IoT and wearables for detection of COVID-19 diagnosis using fusion-based feature extraction with multikernel extreme learning machine
Presently, wearables act as a vital part of healthcare sector and they are able to offer exclusive perceptions about the person's health conditions. In contrast to traditional diagnosis in a hospital environment, wearables can give unrestricted access to real-time physiological data. COVID-19 epidemic is increasing at a faster rate with limited test kits. Hence, it becomes essential to develop a novel COVID-19 diagnostic model. Numerous studies were based on the utilization of artificial intelligence techniques on radiological images to precisely identify the disease. This chapter presents an efficient fusion-based feature extraction with multikernel extreme learning machine (FFE-MKELM) for COVID-19 diagnosis using internet of things (IoT) and wearables. Primarily, the wearables and IoT are used to capture the radiological images of the patient. The presented FFE-MKELM model incorporates Gaussian filtering based preprocessing for removing the noise that exists in the radiological image. Besides, directional local extreme patterns with deep features based on Inception v4 model are applied for the FFE process. In addition, MKELM model is utilized as a classification model to determine the appropriate class label of the input radiological images. Moreover, monarch butterfly optimization algorithm is applied to fine tune the parameters involved in the MKELM model. Experimental validation of the FFE-MKELM model is performed against benchmark dataset and the outcomes are inspected under different measures. The resultant simulation outcome ensured the betterment of the FFE-MKELM method by demonstrating an increased sensitivity of 97.34%, specificity of 97.26%, accuracy of 97.14%, and F-measure of 97.01%. 2022 Elsevier Inc. All rights reserved. -
IOT based air quality sensor device /
Patent Number: 358781-001, Applicant: Dr.Sarwesh P. -
IOT based application for monitoring electricity power consumption in home appliances
Internet of Things is one of the emerging techniques that help in bridging the gap between the physical and cyber world. In the Internet of Things, the different smart objects connected, communicate with each other, data is gathered from the smart objects and based on the need of the users, and the data gathered are queried and sent back to the user. IoT helps in monitoring electrical and physical parameters. Electricity consumption from electronic devices is one among such parameters that need to be monitored. The development of energy efficient schemes for the IoT is a challenging issue as the IoT becomes more complex due to its large scale the current techniques of wireless sensor networks cannot be applied directly to the IoT. To achieve the green networked IoT, this paper proposes a Wi-Fi enabled simple low cost electricity monitoring device that can monitor the electricity consumption on home appliances which helps to analyses the consumption of electricity on a daily and weekly basis. Copyright 2019 Institute of Advanced Engineering and Science. All rights reserved. -
IOT based application to detect fall with a measured force
Fall of patients and aged individuals may end up deadly if unnoticed in time. A fall detection framework has been developed which sends caution notification to the concerned individuals or to the specialist, at the time of occurrence. To limit the consequences of associated wounds/damage caused by the fall, such a device has been developed. The model in this study, detects the fall and measures the force of the fall without using the force sensor and the direction of the fall. In this study, the body posture is obtained from change of increasing speed in three axes, which is measured with a triaxial accelerometer (ADXL335). The sensor is set on the lumbar area to interpret the tilt point. The value obtained from the sensor is compared with the threshold given to diminish the false cautions and furthermore provides the force by which the individual has fallen and the direction in which the person has fallen. The threshold value is computed by the execution of various trials on subjects in different directions of fall. The sensor data is collected on the fall is computed and analyzed in the Audrino microcontroller. The location of fall is detected by GPS beneficiary, which is customized to trace the subject persistently. On detecting the fall, the gadget sends an instant message through GSM module to the emergency contact. The developed model is tested on 7 volunteers who replicated falls in different direction with varying forces. Out of 28 trials, 80% of exactness is accomplished with zero false cautions for dayto-day activities like sitting, lying down on bed and grabbing objects. IAEME Publication. -
IoT Based Bus Identification and Distance Notifier for the Visually Impaired
Public transport is a major obstacle for the visually impaired, and it tends to limit their independence and mobility. To overcome this problem, the current project presents an IoT-based bus identification and distance notification system that is meant to offer real-time support and improve the traveling experience of the visually impaired commuters. The system uses a Raspberry Pi controller in combination with an RFID reader and several RFID cards, each one of which is designated for a particular bus, to detect oncoming vehicles. A GPS unit monitors the position of the bus at all times, so it is possible to calculate exact distances to any of the stops. For additional convenience, the system includes three push-button switches programmed to three predetermined bus stops. Users may pick their destination stop, and the system will offer auditory feedback in terms of distance to the selected destination. Feedback is offered by an earphone, providing hands-free use and receiving instructions without visual interaction. The integration of RFID-based bus identification, GPS location, and voice guidance provides smooth real-time information, less reliant on outside help. The system increases mobility confidence through accurate, timely, and convenient information, enabling blind travelers to make use of public transport independently. Through the combination of IoT technology and assistive technologies, the project enables enhanced accessibility and inclusion in urban mobility systems. 2025 IEEE. -
IoT based car accident detection and notification algorithm for general road accidents
With an increase in population, there is an increase in the number of accidents that happen every minute. These road accidents are unpredictable. There are situations where most of the accidents could not be reported properly to nearby ambulances on time. In most of the cases, there is the unavailability of emergency services which lack in providing the first aid and timely service which can lead to loss of life by some minutes. Hence, there is a need to develop a system that caters to all these problems and can effectively function to overcome the delay time caused by the medical vehicles. The purpose of this paper is to introduce a framework using IoT, which helps in detecting car accidents and notifying them immediately. This can be achieved by integrating smart sensors with a microcontroller within the car that can trigger at the time of an accident. The other modules like GPS and GSM are integrated with the system to obtain the location coordinates of the accidents and sending it to registered numbers and nearby ambulance to notify them about the accident to obtain immediate help at the location. 2019 Insitute of Advanced Engineeering and Science. All rights reserved. -
IoT based continuous monitoring of cardiac patients using Raspberry Pi
In the recent development the Internet of Things (IoT) brings all electronics objects in to a single domain and it is easy to access everything through internet. The applications of IoT are Smart agriculture, Smart Home, Smart City, Smart health monitoring system etc. The automation of health care is one of the application which monitors the patient health status using IoT to make medical equipments more efficient by monitoring the patient's health, in which identifies the body conditions and reduces the human error. A health care monitoring system is used to monitor patient's body parameters for the particular disese and obtain the various values about it. The heart rate monitor is one of the in system using IoT to recognize the cardic patients condition and monitor the status in emergency situations. It monitors the heart rate of the patient with long term cardiovascular disease. Here the Arduino based microcontroller is used to communicate to the sensors such as pulse sensor and ECG Sensor. The system can analyze the signal, extract features from it, detect the normal or abnormal conditions with the help of Raspberry Pi and the results of the ECG signals is sent to the web server. It ensures the signal transmission of heart rate signal to the database through IoT. This also suggests doctors to care the patient follow-up their patient using the patient's data stored in the database. Thus IoT brings one of the solution for cardiac patient monitoring and also reduces the complexity between patient outcome and technology. 2018 Author(s). -
IoT Based Enhanced Safety Monitoring System for Underground Coal Mines Using LoRa Technology
Extracting coal from Underground mine is a hazardous and tough job that needs continuous monitoring of environmental conditions to protect workers health and safety. Though some research works have explored wireless monitoring devices for underground mining, such as ZigBee and Wi-Fi technologies, they come with inherent restraints for instance restricted coverage, susceptibility to interference, reliability issues, security concerns, and high-power consumption. An Enhanced Safety Monitoring System for coal extraction from Underground Mines, employing LoRa communication technology for the effectual transmission of collected data to overcome existing challenges is discussed in this paper. The proposed system consists of two subsystems, one for monitoring the status of miners and another for comprehensive monitoring. LoRaWAN (Long Range Wide Area Network) is a multipoint protocol and this media access control (MAC) enables low-power devices to establish communication with Internet of Things (IoT) applications over extended wireless connections for long-range networks. LoRaWAN operates on lower radio frequencies, thereby providing a longer range of communication. This technology is known for its efficiency in optimizing LPWAN, offering extended range, extended battery life, robustness, and cost-effectiveness, making it highly suitable for industrial mining applications. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
IOT based Green House monitoring system
With industrialization and continuously evolving climatic conditions, the urge to practice agriculture with the fusion of technology has become a necessity. In the era of Internet of Things where all eyes are witnessing the evolution of machine to machine interaction, there is also a lack of clarity in considering the type of protocol to be used in building a particular system like Green House. A green house is a regulated environment for agriculture where critical parameters like temperature, light, humidity, ph level of soil can be monitored with the help of sensor systems using Internet of Things protocols. Message Queue Telemetry Transfer protocol was chosen over Constrained Application Protocol and Extensible Messaging and Presence Protocol in the experiment conducted in terms of its light weight transmission, resource consumption and effectively providing the different quality of services to detect the temperature and humidity as well as the gas leaks encountered in a greenhouse environment. 2018 Tinu Anand Singh and J. Chandra. -
IoT based heart monitoring and alerting system with cloud computing and managing the traffic for an ambulance in India
Global Burden of Disease Report, released in Sept 2017, shows that Cardiovascular Diseases caused 1.7 million deaths (17.8%) in 2016 and it is the leading cause of deaths in India [1]. According to the Indian Heart Association, 25% of all heart attacks happen under the age of 40. In most cases, the initial heart attacks are often ignored. Even post-diagnosis, as per government data [2], 50% of heart attack cases reach the hospital in more than 400 minutes against the ideal window time of 180 minutes; post which damage is irreversible. The delay is often attributed to delay in reaching a hospital or receiving primary aid. In India, traffic conditions also add to the grimace of the situation. Although the government is taking various measures; a holistic solution is required to minimize the delay at each of the steps like accessing the patient situation, contacting the Medical aid or making available the nearest aid possible. In this paper, we aim at providing the holistic solution using the Internet of Things technology (IOT) along with data analytics. IoT enables real-time capturing and computation of medical data from smart sensors built-in wearable devices. The amalgamation of Internet-based services with Medical Things (Smart sensors) enhance the chances of survival of patients. The proposed system analyses the inputs collected from the sensors fit with the patients prone to cardiovascular diseases to ascertain the emergency situation. In addition, to these data, the system also considers age, maximum and minimum heart rate. Based on computational results received from the input parameters, the system triggers the alert to emergency contacts such as the close relatives of the patient, doctors, the hospitals and nearby ambulance. The proposed system combines with the optimized navigation platform to guide the medical assistance to find the fastest route. Copyright 2019 Institute of Advanced Engineering and Science. All rights reserved. -
IoT based hybrid patient health monitoring system
According to the data released by the World Health Organization (WHO). Cardiovascular diseases (CVDs) are still the leading thread worldwide. Every year around 17.9 million fatalities worldwide are attributed to CVDs, making up 31% of overall deceases. Heart attacks and strokes are the main causes account for most of these deaths, and a large fraction happen so early in the age group of under 70. Check-ups are essential to monitor the healthcondition of the elderly people, which generates a considerable challenge to the existing medical field. Subsequently,It's becoming more crucial to diagnose diseases quickly and accurately with an affordable cost. The World's population growth need for a smart and affordable healthcare solutions to reduce the medical costs. Thus, the development of an effective health monitoring system that can quickly identify irregularities in health and provide precise diagnoses based on gathered data is imperative. New developments in cloud computing and mobile technologies have led to the development of a number of cloud-based healthcare products and services. These cloud systems allow for the automatic collection and transmission of medical data to providers from any place, allowing for the network-based delivery of patient feedback. This project's goal is to use the ThingSpeak IoT platform to create and implement an Internet of Things-based patient health monitoring system in order to meet these goals. 2026 Author(s).