Internet of Things Based Smart Hospital Saline Monitoring System

Nagaraj, P.; Muneeswaran, V.; Sudar, K. Muthamil; Ali, R. Sabik; Someshwara, A. L.; Kumar, T. Sangeeth

doi:10.1109/icccsp52374.2021.9465512

Cited by 40 publications

(3 citation statements)

References 14 publications

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“…Accuracy is improved by adding image context information, and system speed is increased by adding other features. On the other hand, the multi-scale system only slightly improves accuracy, and the classifier has little effect on detector quality [21,104,105].…”

Section: Traditional Approaches For Vehicle Detectionmentioning

confidence: 99%

Object Detection in Autonomous Vehicles under Adverse Weather: A Review of Traditional and Deep Learning Approaches

Tahir,

Zhang,

Asim

et al. 2024

Algorithms

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Enhancing the environmental perception of autonomous vehicles (AVs) in intelligent transportation systems requires computer vision technology to be effective in detecting objects and obstacles, particularly in adverse weather conditions. Adverse weather circumstances present serious difficulties for object-detecting systems, which are essential to contemporary safety procedures, infrastructure for monitoring, and intelligent transportation. AVs primarily depend on image processing algorithms that utilize a wide range of onboard visual sensors for guidance and decisionmaking. Ensuring the consistent identification of critical elements such as vehicles, pedestrians, and road lanes, even in adverse weather, is a paramount objective. This paper not only provides a comprehensive review of the literature on object detection (OD) under adverse weather conditions but also delves into the ever-evolving realm of the architecture of AVs, challenges for automated vehicles in adverse weather, the basic structure of OD, and explores the landscape of traditional and deep learning (DL) approaches for OD within the realm of AVs. These approaches are essential for advancing the capabilities of AVs in recognizing and responding to objects in their surroundings. This paper further investigates previous research that has employed both traditional and DL methodologies for the detection of vehicles, pedestrians, and road lanes, effectively linking these approaches with the evolving field of AVs. Moreover, this paper offers an in-depth analysis of the datasets commonly employed in AV research, with a specific focus on the detection of key elements in various environmental conditions, and then summarizes the evaluation matrix. We expect that this review paper will help scholars to gain a better understanding of this area of research.

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Section: Traditional Approaches For Vehicle Detectionmentioning

confidence: 99%

Object Detection in Autonomous Vehicles under Adverse Weather: A Review of Traditional and Deep Learning Approaches

Tahir,

Zhang,

Asim

et al. 2024

Algorithms

View full text Add to dashboard Cite

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“…Non-wearable IoT devices in healthcare are tools not worn on the body but which play a critical role in patient care. These devices, combined with the concept of IoT, offer efficient means for telehealth interventions, improving hospital efficiency, reducing human errors [20], and enhancing patient care. Examples include remote patient monitoring tools and medical dosage monitoring [20].…”

Section: Wearablementioning

confidence: 99%

“…These devices, combined with the concept of IoT, offer efficient means for telehealth interventions, improving hospital efficiency, reducing human errors [20], and enhancing patient care. Examples include remote patient monitoring tools and medical dosage monitoring [20]. Hospital room automation, in-hospital contact tracing, medication management, patient safety, and more can be achieved through the integration of these non-wearable devices into the existing systems [21,22].…”

Section: Wearablementioning

confidence: 99%

Navigating Battery Choices in IoT: An Extensive Survey of Technologies and Their Applications

Hasan,

Tom,

Yuce

2023

Batteries

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In recent years, there has been significant progress in IoT solutions for a variety of fields. The real-time functionality and remote deployment of IoT solutions are two crucial aspects that are necessary for their successful implementation. To achieve this, external batteries play a major role. While lithium–ion batteries are often the go-to choice for IoT devices, it is essential to recognise that different IoT applications have unique needs. Therefore, it is important to conduct a thorough examination of existing battery solutions and their suitability for various IoT applications. This paper presents an extensive survey of different battery technologies, accompanied by an assessment of their applicability in different IoT applications. The aim is to offer a clear and practical guide for researchers and professionals seeking the best battery solutions for their IoT applications.

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