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Keywords = CPLR

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20 pages, 478 KiB  
Article
Time and Memory Efficient Online Piecewise Linear Approximation of Sensor Signals
by Florian Grützmacher, Benjamin Beichler, Albert Hein, Thomas Kirste and Christian Haubelt
Sensors 2018, 18(6), 1672; https://doi.org/10.3390/s18061672 - 23 May 2018
Cited by 19 | Viewed by 4491
Abstract
Piecewise linear approximation of sensor signals is a well-known technique in the fields of Data Mining and Activity Recognition. In this context, several algorithms have been developed, some of them with the purpose to be performed on resource constrained microcontroller architectures of wireless [...] Read more.
Piecewise linear approximation of sensor signals is a well-known technique in the fields of Data Mining and Activity Recognition. In this context, several algorithms have been developed, some of them with the purpose to be performed on resource constrained microcontroller architectures of wireless sensor nodes. While microcontrollers are usually constrained in computational power and memory resources, all state-of-the-art piecewise linear approximation techniques either need to buffer sensor data or have an execution time depending on the segment’s length. In the paper at hand, we propose a novel piecewise linear approximation algorithm, with a constant computational complexity as well as a constant memory complexity. Our proposed algorithm’s worst-case execution time is one to three orders of magnitude smaller and its average execution time is three to seventy times smaller compared to the state-of-the-art Piecewise Linear Approximation (PLA) algorithms in our experiments. In our evaluations, we show that our algorithm is time and memory efficient without sacrificing the approximation quality compared to other state-of-the-art piecewise linear approximation techniques, while providing a maximum error guarantee per segment, a small parameter space of only one parameter, and a maximum latency of one sample period plus its worst-case execution time. Full article
(This article belongs to the Section Sensor Networks)
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807 KiB  
Article
Pupillary Responses to Single and Sinusoidal Light Stimuli in Diabetic Patients
by Wolfgang H. Zangemeister, Thilo Gronow and Ulrich Grzyska
Neurol. Int. 2009, 1(1), e19; https://doi.org/10.4081/ni.2009.e19 - 16 Nov 2009
Cited by 9 | Viewed by 1
Abstract
We examined effects of diabetes mellitus (DM) on the pupillary light reflex (PLR). Phasic pupillary response to a single light stimulus (200 ms) (pPLR) and to continuous sinusoidal stimuli with four different frequencies (0.1, 0.3, 0.7, 1.3Hz) (cPLR) were examined in 52 DM [...] Read more.
We examined effects of diabetes mellitus (DM) on the pupillary light reflex (PLR). Phasic pupillary response to a single light stimulus (200 ms) (pPLR) and to continuous sinusoidal stimuli with four different frequencies (0.1, 0.3, 0.7, 1.3Hz) (cPLR) were examined in 52 DM patients and 21 control subjects. We asked: does recording and frequency analysis of cPLR together with short time fourier [STFT] analysis of pPLR differentiate better between DM patients and normal subjects than pPLR only? Initial pupil diameter was significantly decreased in the DM group. For pPLR. maximal contraction velocity (Vmax), Vmax of redilation 1, reflex-amplitude and pPLR latency were significantly reduced in those patients who also showed signs of diabetic autonomic neuropathy (DNP). Tests of dynamic pupillary light reflex (cPLR) revealed that all DM patients had a significantly reduced gain at lower frequencies. Pupil phase lag was greater at 0.1 and 0.3Hz and smaller at 0.7 and 1.3 Hz in the DNP group (p<0.001). Comparison of single pPLR recordings of 5 DNP patients with 5 subjects using short time fast fourier (STFT) analysis revealed a characteristic change from low frequency content in healthy subjects to high frequency content in DNP patients. Significant changes in the PLR in DM can be found only when symptoms of autonomic neuropathy have been shown. Both sympathetic and the parasympathetic nervous systems are affected by diabetic autonomic neuropathy. Only recording of cPLR , together with STFT of pPLR can identify significant pathological deficits of pupillary control in single cases. Full article
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