scholarly journals Decomposition and modeling of signal shapes of single point cardiac monitoring

2020 ◽  
Vol 6 (3) ◽  
pp. 583-586
Author(s):  
Milad Eyvazi Hesar ◽  
Walid Madhat Munief ◽  
Achim Müller ◽  
Nikhil Ponon ◽  
Sven Ingebrandt

AbstractWe introduce a novel method for electronic recording of cardiac signals from a single point at the skin in contrast to classical differential electrocardiography (ECG). Ultralownoise transistor devices with an adaptable, auto-stabilizing transimpedance amplifier are able to measure tiny skin potential modulations from a single contact electrode located at an individual’s wrist (single-point cardiography-SPC). Although SPC signals were highly prone to interspersed noise, they contained periodic patterns. In an electromagnetically shielded setting, we could clearly extract breathing and cardiac rhythms from the acquired SPC signals. As the reference, we measured ECG in parallel. Several signal-processing techniques like smoothening, correlation, decomposition and signal extraction showed that SPC signals contain breathing and periodic heart potential variations, which are time-correlated with ECG. In the future, we intent to use this novel technique to measure heart signals from patients in different health conditions.

Author(s):  
Sun Kim ◽  
Karl B. Ousterhout

Abstract In most machining processes, large amounts of energy are needed to accomplish the machining operation. When this energy is transmitted through a structure that has minimal damping characteristics, such as a lathe or a milling machine, self sustained oscillations (chatter) can develop. When chatter develops, it can be viewed as a basic performance limitation of the machine tool. In order to suppress the chatter, a real-time controller using digital signal processing techniques has been implemented. This paper discusses a novel method for the real time computation of the transfer function of the machine tool-workpiece combination and illustrates how a real-time active chatter controller can be designed and integrated into existing machine tools to overcome this performance limitation.


2017 ◽  
Author(s):  
Sujeet Patole ◽  
Murat Torlak ◽  
Dan Wang ◽  
Murtaza Ali

Automotive radars, along with other sensors such as lidar, (which stands for “light detection and ranging”), ultrasound, and cameras, form the backbone of self-driving cars and advanced driver assistant systems (ADASs). These technological advancements are enabled by extremely complex systems with a long signal processing path from radars/sensors to the controller. Automotive radar systems are responsible for the detection of objects and obstacles, their position, and speed relative to the vehicle. The development of signal processing techniques along with progress in the millimeter- wave (mm-wave) semiconductor technology plays a key role in automotive radar systems. Various signal processing techniques have been developed to provide better resolution and estimation performance in all measurement dimensions: range, azimuth-elevation angles, and velocity of the targets surrounding the vehicles. This article summarizes various aspects of automotive radar signal processing techniques, including waveform design, possible radar architectures, estimation algorithms, implementation complexity-resolution trade-off, and adaptive processing for complex environments, as well as unique problems associated with automotive radars such as pedestrian detection. We believe that this review article will combine the several contributions scattered in the literature to serve as a primary starting point to new researchers and to give a bird’s-eye view to the existing research community.


Sign in / Sign up

Export Citation Format

Share Document