International Telemetering Conference Proceedings, Volume 56 (2021)
http://hdl.handle.net/10150/665984
2024-03-28T14:57:26ZInternational Telemetering Conference Proceedings, Volume 56 (2021)
http://hdl.handle.net/10150/666311
International Telemetering Conference Proceedings, Volume 56 (2021)
2021-10-01T00:00:00ZPseudo-Electrical Alternans: Beyond Pericardial Effusion
http://hdl.handle.net/10150/666310
Pseudo-Electrical Alternans: Beyond Pericardial Effusion
Jaina, Akhil; Kaurb, Parneet; Gasparyanc, Lilit; Jindald, Rishabh; Kelaiyae, Arjun; Popatf, Apurva; Miranig, Zankhan; Buragamadagua, Bhanusowmya; Jain, Siddharth
Electrical alternans on ECG is reported in substantial pericardial effusion. Pseudo Electrical alternans (pseudoEA) is the alternation in the QRS amplitude in the absence of pericardial effusion. We reviewed 16 such cases of pseudoEA (26-72 years, 68.75% males, 31.25% females). Besides physiological causes, cardiac diagnosis included arrhythmia (31.25%), coronary artery disease (18.75%), congestive heart failure (12.5%) in our review. The most common non-cardiac diagnosis was bronchial asthma. PseudoEA in both chest and limb leads was seen in 42.8%, chest leads alone in 35.7%, and limb leads alone in 21.4%. Telemetry surveillance is useful in identifying pseudoEA and confirms it by its reversal after treating the main pathology or removing the causing agent. There should be a high index of suspicion amongst physicians when electrical alternans is present on telemetry to identify and treat the alternative conditions in the absence of pericardial effusion.
2021-10-01T00:00:00ZEdge Machine Learning for Face Detection
http://hdl.handle.net/10150/666309
Edge Machine Learning for Face Detection
Cooper, Geffen; Manjunath, B.S.; Isukapalli, Yogananda
This paper describes an implementation of edge machine learning for vision-based classification and detection tasks. In edge machine learning, machine and deep learning algorithms are executed locally on embedded devices rather than on more powerful computers or the cloud. The main task explored is face detection using a low-power microcontroller. This device utilizes a convolutional neural network (CNN) accelerator that optimizes convolution and pooling operations for fast power-efficient inference. Development for this system requires building and training a hardwarelimited CNN rather than fine-tuning a pre-trained state-of-the-art model. The development process is discussed along with the constraints of this embedded device.
2021-10-01T00:00:00ZAn Experiment on Energy Harvesting for Aircraft Instrumentation
http://hdl.handle.net/10150/666308
An Experiment on Energy Harvesting for Aircraft Instrumentation
Giullian, Amy
Sensor installation for flight test instrumentation is a difficult process because the sensors must be wired to a central power unit. A small power source for transducers would make the installation process more efficient. This paper investigates the power output of a piezoelectric energy harvester. An experiment was conducted using a piezoelectric diaphragm connected to a full-wave bridge rectifier. The circuit is analyzed and experimental results are presented. The results are analyzed to determine if the output power is sufficient to supply a small transducer.
2021-10-01T00:00:00Z