scholarly journals Underwater Telemetry Sensory Mechanisms

2021 ◽  
Author(s):  
Andrew Kamal
Keyword(s):  
Wireless Communication ◽  
Real Time ◽  
Signal Strength ◽  
Experimental Methods ◽  
Aquatic Environments ◽  
Time Sensitivity ◽  
Noticeable Improvement ◽  
Real Time Detection ◽  
Underwater Telemetry ◽  
Underwater Wireless Communication

There are various different methods and apparatuses in relation to detecting harmful toxins within our ocean and other aquatic environments. The need is for the best approach given the time sensitivity of the matter. Through looking at my past designs, and other buoy deployments, there may be a way to improve detection in regards to signal strength capability. This requires a thorough understanding and overview of software defined networking capabilities centered under underwater wireless communication and telemetry, as well as methods of improvement in regards to real time detection. There also needs to be an emphasis on the real time detection of data and its importance. The likelihood of a noticeable improvement is centered on the idea of a P2P wireless and/or mesh networking implementation to improve signal latency and strength. There shall also be an emphasis on experimental methods as well and protocols of improvement or further implementation. Due to the doppler effect on waves, and the difficulty for traditional signal processing and strength to happen underwater, research within the field of underwater acoustics and wireless communication is crucial.

The Implantable Internet of Medical Things

2021 ◽  
Vol 24 (3) ◽  
pp. 20-25
Author(s):  
Raffaele Guida ◽  
Neil Dave ◽  
Francesco Restuccia ◽  
Emrecan Demirors ◽  
Tommaso Melodia
Keyword(s):  
Wireless Communication ◽  
Real Time ◽  
Medical Devices ◽  
Experimental Evaluation ◽  
Closed Loop ◽  
Single Charge ◽  
Research Gap ◽  
Internet Of Medical Things ◽  
End To End ◽  
Real Time Detection

The promise of real-time detection and response to life-crippling diseases brought by the Implantable Internet of Medical Things (IIoMT) has recently spurred substantial advances in implantable technologies. Yet, existing medical devices do not provide at once the miniaturized end-to-end body monitoring, wireless communication and remote powering capabilities to implement IIoMT applications. This paper fills the existing research gap by presenting U-Verse, the first FDA-compliant rechargeable IIoMT platform packing sensing, computation, communication, and recharging circuits into a penny-scale platform. Extensive experimental evaluation indicates that U-Verse (i) can be wirelessly recharged and can store energy several orders of magnitude more than state-of-theart capacity in tens of minutes; (ii) with one single charge, it can operate from few hours to several days. Finally, U-Verse is demonstrated through (i) a closed-loop application that sends data via ultrasounds through real porcine meat; and (ii) a real-time reconfigurable pacemaker.

Real-Time Detection of Drowsiness Related Lane Departures Using Steering Wheel Angle

2012 ◽  
Author(s):  
Anthony D. McDonald ◽  
Chris Schwarz ◽  
John D. Lee ◽  
Timothy L. Brown
Keyword(s):  
Real Time ◽  
Steering Wheel ◽  
Real Time Detection

Real-time Detection of Aortic Valve in Echocardiography using Convolutional Neural Networks

2020 ◽  
Vol 16 (5) ◽  
pp. 584-591 ◽  
Author(s):  
Muhammad Hanif Ahmad Nizar ◽  
Chow Khuen Chan ◽  
Azira Khalil ◽  
Ahmad Khairuddin Mohamed Yusof ◽  
Khin Wee Lai
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Heart Disease ◽  
Aortic Valve ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Valvular Heart Disease ◽  
Detection System ◽  
Processing Unit ◽  
Real Time Detection

Background: Valvular heart disease is a serious disease leading to mortality and increasing medical care cost. The aortic valve is the most common valve affected by this disease. Doctors rely on echocardiogram for diagnosing and evaluating valvular heart disease. However, the images from echocardiogram are poor in comparison to Computerized Tomography and Magnetic Resonance Imaging scan. This study proposes the development of Convolutional Neural Networks (CNN) that can function optimally during a live echocardiographic examination for detection of the aortic valve. An automated detection system in an echocardiogram will improve the accuracy of medical diagnosis and can provide further medical analysis from the resulting detection. Methods: Two detection architectures, Single Shot Multibox Detector (SSD) and Faster Regional based Convolutional Neural Network (R-CNN) with various feature extractors were trained on echocardiography images from 33 patients. Thereafter, the models were tested on 10 echocardiography videos. Results: Faster R-CNN Inception v2 had shown the highest accuracy (98.6%) followed closely by SSD Mobilenet v2. In terms of speed, SSD Mobilenet v2 resulted in a loss of 46.81% in framesper- second (fps) during real-time detection but managed to perform better than the other neural network models. Additionally, SSD Mobilenet v2 used the least amount of Graphic Processing Unit (GPU) but the Central Processing Unit (CPU) usage was relatively similar throughout all models. Conclusion: Our findings provide a foundation for implementing a convolutional detection system to echocardiography for medical purposes.

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