<p>The proliferation of efficient edge computing has enabled a paradigm shift of how we monitor and interpret urban air quality. Coupled with the dense spatiotemporal resolution realized from largescale wireless sensor networks, we can achieve highly accurate realtime local inference of airborne pollutants. In this paper, we introduce a novel Deep Neural Network architecture targeted at latent timeseries regression tasks from continuous, exogenous sensor measurements, based on the Transformer encoder scheme and designed for deployment on lowcost powerefficient edge processors.
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A Cudoped TiO2x nanoscale memristor is fabricated, whose faithful mathematical model is established based on the memristive behaviors and its switching mechanism. Using this model, a chaotic system is constructed and its complex dynamics are investigated by numerical simulations. Furthermore, hardware experiments are also designed to verify the model in chaotic circuit.
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This dataset is proposed for human activity recognition tasks. The static activities including sitting, standing, and laying, as well as walking, running, cycling, and walking upstairs/downstairs. Each activity lasts for 2 minutes, 50 subjects were involved in the experiments.
Twentythree subjects were involved in 8 activities, including 3 static ones and 5 periodic activities of walking, running, cycling, and walking upstairs/downstairs. Each activity lasts around 2 minutes.
For each column, the sensor signals are organized as " 'wri_Acc_X', 'wri_Acc_Y', 'wri_Acc_Z', 'wri_Gyr_X', 'wri_Gyr_Y', 'wri_Gyr_Z', 'wri_Mag_X', 'wri_Mag_Y', 'wri_Mag_Z', 'ank_Acc_X', 'ank_Acc_Y', 'ank_Acc_Z', 'ank_Gyr_X', 'ank_Gyr_Y', 'ank_Gyr_Z', 'ank_Mag_X', 'ank_Mag_Y','ank_Mag_Z', 'bac_Acc_X', 'bac_Acc_Y', 'bac_Acc_Z', 'bac_Gyr_X', 'bac_Gyr_Y', 'bac_Gyr_Z', 'bac_Mag_X', 'bac_Mag_Y', 'bac_Mag_Z' ".
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A qualitative and quantitative extension of the chaotic models used to generate selfsimilar traffic with longrange dependence (LRD) is presented by means of the formulation of a model that considers the use of piecewise affine onedimensional maps. Based on the disaggregation of the temporal series generated, a valid explanation of the behavior of the values of Hurst exponent is proposed and the feasibility of their control from the parameters of the proposed model is shown.
fGn series used for simulations in the article "Sobre la Generación de Tráfico Autosimilar con Dependencia de Largo Alcance Empleando Mapas Caóticos Unidimensionales Afines por Tramos (Versión Extendida)", "On the Generation of Selfsimilar with Longrange Dependent Traffic Using Piecewise Affine Chaotic Onedimensional Maps (Extended Version)". Available at:
https://arxiv.org/abs/2104.04135.
https://easychair.org/publications/preprint/Xwx3.
They should be used in MATLAB R2009a.
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This Matlab model and the included results are submitted as reference for the paper ''.
Presenting a comparative study of the Sequential Unscented Kalman Filter (SUKF), Leastsquares (LS) Multilateration and standard Unscented Kalman Filter (UKF) for localisation that relies on sequentially received datasets.
The KEWLS and KKF approach presents a novel solution using Linear Kalman Filters (LKF) to extrapolate individual sensor measurements to a synchronous point in time for use in LS Multilateration.
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This article explores the required amount of time series points from a highspeed computer network to accurately estimate the Hurst exponent. The methodology consists in designing an experiment using estimators that are applied to time series addresses resulting from the capture of highspeed network traffic, followed by addressing the minimum amount of point required to obtain in accurate estimates of the Hurst exponent.
fGn series used for simulations in the article "Preliminaries on the Accurate Estimation of the Hurst Exponent Using Time Series". Available at:
https://arxiv.org/abs/2103.02091.
https://www.techrxiv.org/articles/preprint/Preliminaries_on_the_Accurate....
https://easychair.org/publications/preprint/RQsp.
They should be used in Selfis01b.
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This page is a 3 part submission, for which 3 open access pages are used. Research Papers related with each part are mentioned in that part page only.
1) This is an open access, so everything can be downloaded after login (free signup). You have to click on 'Title'.
2) This is divided in Three Parts  All parts are open access ,so everything can be downloaded after login. PartII https://dx.doi.org/10.21227/b58ynb96 and PartIII https://dx.doi.org/10.21227/pvvx7p34.
3) These models has been made by me, in order to help doctors working in this field or covid or vaccinated patients.I am as such not for medical research but feedback (not from propaganda people, even negative) from clinical medical doctors and pharmacologists can be given,because I have made this for them only.
4) The ßBio models are made after going through clinical textbooks, and some published research papers but mostly clinical textbooks (mentioned in the references of papers , bibliography).
5) Mathematicians can check whether mathematics is valid or not. The physics, biological, chemistry and engineering results can be checked by their experts. But if they ask reference, they dont understand I have claimed this as originality, Mywork,My proposition, how can it has reference but elementary all books etc mentioned. Ridiculous expecting 100200 pages theory in 8 pages,4 pages, then demand more explanation, which is already in textbooks when they themselves are professors.
6) There may be improved version But I m old now, cannot carry this further.
7) Cheatsheets, Common Libraries,Fundamental Theory(made by me only) are given in PartI only rather than PartsII and III, so that huge dataset, models, all can be comprehensible to doctors, professors, researchers.
8) These are not clinically tested but models have used patients data and the comparison results are in the paper.
9) As such, No other question or email will be replied. I may have left completely R&D or other reason.
Attachments (CommonPart I,II,III)
 General_ReadMe. pdf
 System Requirements.pdf
 Models_Libraries Installation.pdf has installation guide for Windows,Mac,Linux. These models originally are made on Linux
 Model Versions : list of supported version . Earliest version support tested is 2013 but it may even work on earlier versions
 How use Dataset Files (CommonPart I,II,III)
BBio Models Libraries (CommonPart I,II,III)
These libraries are the common building blocks
 ß_model_
 ß_model_
 ß_model_
 ß_model_
 ß_model_
 ß_model_
 ß_model_
 ß_model_
BBio Models CheatSheets (Part I,II,III)
These cheat sheets are script in c language , useful for transferring to other sofwares or making GUI or converting to opensource.
 ß_cheatsheet_Heart
 ß_cheatsheet_Heart(male)
 ß_cheatsheet_Heart(female)
 ß_cheatsheet_Lungs
 ß_cheatsheet_Lungs(female)
 ß_cheatsheet_Ear
 ß_cheatsheet_CNS
 ß_cheatsheet_Urinary
1. Dataset Files  'Heart' (further processed results)
Research paper with this dataset : Pattern and in
IEEE Codeocean for this dataset

Heart_process_readme.pdf has details, instructions and how to use , for this dataset.

Heart_process.zip has
2. Dataset Files  'Lungs' (further processed results)
Research paper with this dataset
 Pattern and in
 Pattern and in
 Pattern and in
IEEE Codeocean for this dataset

Lungs_process_readme.pdf has details, instructions and how to use , for this dataset.

Lungs_process.zip has
Paper Citing : If want to cite this in paper etc. ,please refer DoI and/or this url.
Funding: There are no funders for this submission. The author has himself fully selffinanced (with only objective to help). I expect all these papers, would be nice Shroud for the passion and the price paid.
Acknowledgement : The author has generated this on Linux and had even used IEEE partner Code Ocean  Python,C, Matlab ,Cloud Workstation, Jupyter Notebook,Rstudio,stata,julia,Tensorflow, pandas,trial (evaluation) of many proprietary softwares. No paid research, personal R&D work with no support, wastage of time in self teaching,computer crashes/problems and other obstacles.Few gave trial (evaluation) sw with 25 months with even willing for 36 months further extension but didnt accepted hire contract request (the names cannot be disclosed & word of acknowledging expired in duration). No industry or academic will use their time only doing this work, even if given free unless financed or top MNC. The author does not have any special name to be acknowledged.
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The data set contains electrical and mechanical signals from experiments on threephase induction motors. The experimental tests were carried out for different mechanical loads on the induction motor axis and different severities of broken bar defects in the motor rotor, including data regarding the rotor without defects. Ten repetitions were performed for each experimental condition.
Experimental Setup:
The experimental workbench consists of a threephase induction motor coupled with a directcurrent machine, which works as a generator simulating the load torque, connected by a shaft containing a rotary torque wrench.
 Induction motor: 1hp, 220V/380V, 3.02A/1.75A, 4 poles, 60 Hz, with a nominal torque of 4.1 Nm and a rated speed of 1715 rpm. The rotor is of the squirrel cage type composed of 34 bars.
 Load torque: is adjusted by varying the field winding voltage of direct current generator. A singlephase voltage variator with a filtered fullbridge rectifier is used for the purpose. An induction motor was tested under 12.5, 25, 37.5, 50, 62.5, 75, 87.5 and 100% of full load.
 Broken rotor bar: to simulate the failure on the threephase induction motor's rotor, it was necessary to drill the rotor. The rupture rotor bars are generally adjacent to the first rotor bar, 4 rotors have been tested, the first with a break bar, the second with two adjacent broken bars, and so on rotor containing four bars adjacent broken.
Monitoring condition:
All signals were sampled at the same time for 18 seconds for each loading condition and ten repetitions were performed from transient to steady state of the induction motor.
 mechanical signals: five axial accelerometers were used simultaneously, with a sensitivity of 10 mV/mm/s, frequency range from 5 to 2000Hz and stainless steel housing, allowing vibration measurements in both drive end (DE) and nondrive end (NDE) sides of the motor, axially or radially, in the horizontal or vertical directions.
 electrical signals: the currents were measured by alternating current probes, which correspond to precision meters, with a capacity of up to 50ARMS, with an output voltage of 10 mV/A, corresponding to the Yokogawa 96033 model. The voltages were measured directly at the induction terminals using voltage points of the oscilloscope and the manufacturer Yokogawa.
Data Set Overview:
 Threephase Voltage
 Threephase Current
 Five Vibration Signals
Acknowledgements:
The database was acquired in the Laboratory of Intelligent Automation of Processes and Systems and Laboratory of Intelligent Control of Electrical Machines, School of Engineering of São Carlos of the University of São Paulo (USP), Brazil.
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The dataset consists of two populations of fetuses: 160 healthy and 102 Late Intra Uterine Growth Restricted (IUGR). Late IUGR is an adverse pathological condition encompassing chronic hypoxia as a consequence of placental insufficiency, resulting in an abnormal rate of fetal growth. In standard clinical practice, Late IUGR diagnosis can only be suspected in the third trimester and ultimately confirmed at birth. This data collection comprises of a set of 31 Fetal Heart Rate (FHR) indices computed at different time scales and domains accompanied by the clinical diagnosis.
The data for healthy and Late IUGR populations are included in a single .xlsx file.
Participants are listed by rows and features by columns. In the following we report an exhaustive list of features contained in the dataset accompanied by their units, time interval employed for the computation, and scientific literature references:
Fetal and Maternal Domains
 Clinical Diagnosis [HEALTHY/LATE IUGR]: binary variable to report the clinical diagnosis of the participant
 Gestational Age [days]: gestational age at the time of CTG examination
 Maternal Age [years]: maternal age at the time of CTG examination
 Sex [Male (1)/Female (2)]: fetal sex
Morphological and Time Domains
 Mean FHR [bpm] – 1min epoch: the mean of FHR excluding accelerations and decelerations
 Std FHR [bpm] – 1min epoch: the standard deviation of FHR excluding accelerations and decelerations
 DELTA [ms] – 1min epoch: defined in accordance with [1], [2] excluding accelerations and decelerations
 II [] – 1min epoch: defined in accordance with [1], [2] excluding accelerations and decelerations
 STV [ms] – 1min epoch: defined in accordance with [1], [2] excluding accelerations and decelerations
 LTI [ms] – 3min epoch: defined in accordance with [1], [2] excluding accelerations and decelerations
 ACC_L [#] – entire recording: the count of large accelerations defined in accordance with [3], [4]
 ACC_S [#] – entire recording: the count of small accelerations defined in accordance with [3], [4]
 CONTR [#]– entire recording: the count of contractions defined in accordance with [3], [4]
Frequency Domain
 LF [ms²/Hz] – 3min epoch: defined in accordance with [2], LF band is defined in the range [0.03  0.15] Hz
 MF [ms²/Hz] – 3min epoch: defined in accordance with [2], MF band is defined in the range [0.15  0.5] Hz
 HF [ms²/Hz] – 3min epoch: defined in accordance with [2], HF band is defined in the range HF [0.5  1 Hz]
Complexity Domain
 ApEn [bits] – 3min epoch: defined in accordance with [5], m = 1, r = 0.1*standard deviation of the considered epoch
 SampEn [bits] – 3min epoch: defined in accordance with [6], m = 1, r = 0.1*standard deviation of the considered epoch
 LCZ_BIN_0 [bits] – 3min epoch: defined in accordance with [7], binary coding and p = 0
 LCZ_TER_0 [bits] – 3min epoch: defined in accordance with [7], tertiary coding and p = 0
 AC/DC/DR [bpm] – entire recording: defined in accordance with [8]–[10], considering different combinations of parameters T and s, L is constant and equal 100 samples; e.g, AC_T1_s2 is defined as the acceleration capacity computed setting the parameters T = 1 and s = 2
References
[1] D. Arduini, G. Rizzo, A. Piana, P. Bonalumi, P. Brambilla, and C. Romanini, “Computerized analysis of fetal heart rate—Part I: description of the sys tem (2CTG),” J Matern Fetal Invest, vol. 3, pp. 159–164, 1993.
[2] M. G. Signorini, G. Magenes, S. Cerutti, and D. Arduini, “Linear and nonlinear parameters for the analysis of fetal heart rate signal from cardiotocographic recordings,” IEEE Trans. Biomed. Eng., vol. 50, no. 3, pp. 365–374, 2003.
[3] FIGO, “Guidelines for the Use of Fetal Monitoring,” Int. J. Gynecol. Obstet., vol. 25, pp. 159–167, 1986.
[4] R. Rabinowitz, E. Persitz, and E. Sadovsky, “The relation between fetal heart rate accelerations and fetal movements.,” Obstet. Gynecol., vol. 61, no. 1, pp. 16–18, 1983.
[5] S. M. Pincus and R. R. Viscarello, “Approximate entropy: a regularity measure for fetal heart rate analysis.,” Obstet. Gynecol., vol. 79, no. 2, pp. 249–55, 1992.
[6] D. E. Lake, J. S. Richman, M. P. Griffin, and J. R. Moorman, “Sample entropy analysis of neonatal heart rate variability,” Am. J. Physiol.  Regul. Integr. Comp. Physiol., vol. 283, no. 3, pp. R789–R797, 2002.
[7] A. Lempel and J. Ziv, “On the complexity of finite sequences,” IEEE Trans. Inf. Theory, vol. 22, no. 1, pp. 75–81, 1976.
[8] A. Bauer et al., “Phaserectified signal averaging detects quasiperiodicities in nonstationary data,” Phys. A Stat. Mech. its Appl., vol. 364, pp. 423–434, 2006.
[9] A. Fanelli, G. Magenes, M. Campanile, and M. G. Signorini, “Quantitative assessment of fetal wellbeing through ctg recordings: A new parameter based on phaserectified signal average,” IEEE J. Biomed. Heal. Informatics, vol. 17, no. 5, pp. 959–966, 2013.
[10] M. W. Rivolta, T. Stampalija, M. G. Frasch, and R. Sassi, “Theoretical Value of Deceleration Capacity Points to Deceleration Reserve of Fetal Heart Rate,” IEEE Trans. Biomed. Eng., pp. 1–10, 2019.
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This dataset aims at providing a toy demo for the comparison between the doubly orthogonal matching pursuit (DOMP) and its predecessor, the OMP.
File descriptions:
 toydemo.m: Main file. It provides a comparison of the algorithms by loading the measurement and performing a behavioral model of the PA.
 demo_meas.mat: example of the input and output measurements of a power amplifier (PA) working under a 15MHz LTE signal sampled at a sampling rate of 92.16MHz.
 model_gmp_domp_omp.m: generates the model structure and calls the pruning techniques.
 omp_domp.m: executes the OMP and DOMP techniques.
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