3,875 research outputs found
Pulse Signal System: Sensing, Data Acquisition and Body Area Network
Heart rate variability (HRV) is an important physiological signal of the human body, which
can serve as a useful biomarker for the cardiovascular health status of an individual. There are
many methods to measure the HRV using electrical devices, such as ECG and PPG etc. This work
presents a novel HRV detection method which is based on pressure detection on the human wrist.
This method has been compared with existing HRV detection methods.
In this work, the proposed system for HRV detection is based on polyvinylidene difluoride
(PVDF) sensor, which can measure tiny pressure on its surface. Three PVDF sensors are mounted
on the wrist, and a three-channel conditioning circuit is used to amplify signals generated by the
sensors. An analog-to-digital converter and Arduino microcontroller are used to sample and process
the signal. Based on the obtained signals, the HRV can be processed and detected by the
proposed PVDF-sensor-based system.
Another contribution of this work is in designing a wireless body area network (WBAN) to
transmit data acquired on the human body. This WBAN combines two different wireless network
protocols, for both efficient power consumption and data rate. Bluetooth Low Energy protocol is
used for transmitting data from the microcontroller to a personal device, and Wi-Fi is used to send
data to other terminals. This provides the potential for remote HRV signal monitoring.
A dataset consisting of two subjects was used to experimentally validate the proposed system
design and signal processing method. ECG signals are acquired from subjects with wrist pulse
signals for comparison as standard signal. The waveforms of ECG signals and wrist pulse signals
are compared and HRV values are calculated from these two signals separately. The result shows
that HRV calculated by wrist pulse has low error rate. A test of movement effect shows the sensor
can resist mild motions of wrist. Some future improvements of system design and further signal
processing methods are also discussed in the last chapter
Like-sign Di-lepton Signals in Higgsless Models at the LHC
We study the potential LHC discovery of the Z1 KK gauge boson unitarizing
longitudinal W+W- scattering amplitude. In particular, we explore the decay
mode Z1->t tbar along with Z1-> W+W- without specifying the branching
fractions. We propose to exploit the associated production pp-> W Z1, and
select the final state of like-sign dileptons plus multijets and large missing
energy. We conclude that it is possible to observe the Z1 resonance at a 5
sigma level with an integrated luminosity of 100 inverse fb at the LHC upto 650
GeV for a dominant WW channel, and 560 GeV for a dominant ttbar channel.Comment: 13 pages, 7 figure
Changes from Classical Statistics to Modern Statistics and Data Science
A coordinate system is a foundation for every quantitative science,
engineering, and medicine. Classical physics and statistics are based on the
Cartesian coordinate system. The classical probability and hypothesis testing
theory can only be applied to Euclidean data. However, modern data in the real
world are from natural language processing, mathematical formulas, social
networks, transportation and sensor networks, computer visions, automations,
and biomedical measurements. The Euclidean assumption is not appropriate for
non Euclidean data. This perspective addresses the urgent need to overcome
those fundamental limitations and encourages extensions of classical
probability theory and hypothesis testing , diffusion models and stochastic
differential equations from Euclidean space to non Euclidean space. Artificial
intelligence such as natural language processing, computer vision, graphical
neural networks, manifold regression and inference theory, manifold learning,
graph neural networks, compositional diffusion models for automatically
compositional generations of concepts and demystifying machine learning
systems, has been rapidly developed. Differential manifold theory is the
mathematic foundations of deep learning and data science as well. We urgently
need to shift the paradigm for data analysis from the classical Euclidean data
analysis to both Euclidean and non Euclidean data analysis and develop more and
more innovative methods for describing, estimating and inferring non Euclidean
geometries of modern real datasets. A general framework for integrated analysis
of both Euclidean and non Euclidean data, composite AI, decision intelligence
and edge AI provide powerful innovative ideas and strategies for fundamentally
advancing AI. We are expected to marry statistics with AI, develop a unified
theory of modern statistics and drive next generation of AI and data science.Comment: 37 page
The Impact of Internet on Education: Towards an Emerging Paradigm
In today’s e-commerce era, more than ever, we need to equip our students to be independent on-line learners. In this article, we will look into the use of online education as a means of understanding the emerging educational paradigm (from a teacher to a student-focus paradigm). Specifically, the focus is on the secondary school system in which the aim is to comprehend and explore the reasons behind the emerging trend, the flaws in the existing schooling system, the characteristics and effects of our proposed paradigm. Finally, we conclude that the proposed educational paradigm whereby online education is provided to supplement the current traditional classroom based teaching could become a reality sooner than expected
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