Implementation of 5-Block Convolutional Neural Network (CNN) for Saliency Improvement on Flying Object Detection in Videos

Pandey, Shubha; Sharma, Ritu; Singh, Ghanapriya

doi:10.1109/icetce48199.2020.9091773

Cited by 5 publications

(2 citation statements)

References 13 publications

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“…Existing methods for tracking satellites in orbit, among other flying objects, include passive radar signal analysis for drone and air-target detection [49,50], infrared thermal image processing for tracking hot flying objects [51], as well as visible light computer vision [52]. Another notable technology which detects aerial objects is Israel's Iron Dome [53], which utilises active radar technology to detect ballistic missiles mid-air before launching missiles that destroy the target missile mid-air.…”

Section: Introductionmentioning

confidence: 99%

TrackInk: An IoT-Enabled Real-Time Object Tracking System in Space

Aume

Andrews

Pal

et al. 2022

Sensors

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Nowadays, there is tremendous growth in the Internet of Things (IoT) applications in our everyday lives. The proliferation of smart devices, sensors technology, and the Internet makes it possible to communicate between the digital and physical world seamlessly for distributed data collection, communication, and processing of several applications dynamically. However, it is a challenging task to monitor and track objects in real-time due to the distinct characteristics of the IoT system, e.g., scalability, mobility, and resource-limited nature of the devices. In this paper, we address the significant issue of IoT object tracking in real time. We propose a system called ‘TrackInk’ to demonstrate our idea. TrackInk will be capable of pointing toward and taking pictures of visible satellites in the night sky, including but not limited to the International Space Station (ISS) or the moon. Data will be collected from sensors to determine the system’s geographical location along with its 3D orientation, allowing for the system to be moved. Additionally, TrackInk will communicate with and send data to ThingSpeak for further cloud-based systems and data analysis. Our proposed system is lightweight, highly scalable, and performs efficiently in a resource-limited environment. We discuss a detailed system’s architecture and show the performance results using a real-world hardware-based experimental setup.

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Section: Introductionmentioning

confidence: 99%

TrackInk: An IoT-Enabled Real-Time Object Tracking System in Space

Aume

Andrews

Pal

et al. 2022

Sensors

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“…The sensors on wearable device provide raw data on which various signal processing algorithms such as integer wavelet transform (Singh, Singh, et al, 2020b), local mean decomposition (Kaloni et al, 2021), correlation based features (Gupta et al, 2021), are derived so as to eliminate unwanted noise and extract useful information. These features are then provided to various algorithms such as machine learning classifiers (Singh et al, 2018), transfer learning (Agarwal et al, 2020), deep learning (DL) algorithms (Pandey et al, 2020), evolutionary algorithms (Singh & Rawat, 2013) and so forth that has the capability to intelligently and automatically assign test windowed time series signal into different classes.…”

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confidence: 99%

Stress recognition with multi‐modal sensing using bootstrapped ensemble deep learning model

2023

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The factors that influence a person's mental health are numerous, interconnected, and multi-dimensional. Recognition of stress is one of the facets in developing the Mental Healthcare (MHC) system framework. With the advent of technology, smart wearable devices have paved a way to collect data in real-time to provide the cutting-edge reports about the individual. Due to the physiological sensors present in the smart wearable devices, it is now possible to have a robust system to recognize the stress of the smart wearable devices user thus consecutively leading to recognition of factors in leading to stress. However, the current MHC system for recognition and identification of stress have several drawbacks. First, stress recognition is mostly designed for a particular group of individuals like occupational stress, perinatal maternal stress, or health worker stress and fails to propose a framework that would not be targeted for a particular group of individual. Second, most of the previous work done on stress recognition focuses on the extraction of handcrafted features thus requiring human intervention and expertise. To address these issues, this study proposes, a hybrid deep learning based ensemble approach for automated extraction of features and classification into various state of stress for MHC system. The proposed framework takes input from wearable physiological sensors and is provided to deep learning classifier of convolutional neural network (CNN) and CNN-long short term memory based ensemble model. The proposed framework has been experimented on the wearable stress and affect detection dataset and reports an accuracy of 91.52% that is 7.20% higher than earlier reported accuracies from other machine learning and deep learning models.

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