An Improved SDIF Radar Pulse Signal Main Sorting Algorithm

2013 ◽  
Vol 710 ◽  
pp. 637-641 ◽  
Author(s):  
Wei Hua Xiao ◽  
Hong Chao Wu ◽  
Cheng Zhi Yang ◽  
Mei Ling Wang
Keyword(s):  
Real Time ◽  
Pulse Signal ◽  
Radar Signal ◽  
Sorting Algorithm ◽  
Histogram Method ◽  
Transform Method ◽  
Sorting Method ◽  
The Poor ◽  
The Difference ◽  
Difference Histogram

In repetition frequency sorting method, sorting capability of the sequence of the difference histogram method (SDIF) to the PRI jitter radar pulses is poor. Although the PRI transform method can sort PRI jitter radar signal, the amount of computation overlarge is difficult to meet the demand for real-time sorting. Because of the poor sorting capability of SDIF algorithm to jitter PRI, this paper presents an improved algorithm of SDIF, sorts PRI jitter radar signal well, can meet the needs of real-time sorting.

A Method for Real-Time Radar Signal Main Sorting

2013 ◽  
Vol 760-762 ◽  
pp. 567-571
Author(s):  
Hong Chao Wu ◽  
Wei Hua Xiao ◽  
Jian Feng Pu
Keyword(s):  
Real Time ◽  
Pulse Sequence ◽  
High Accuracy ◽  
Radar Signal ◽  
Sorting Algorithm ◽  
The Real ◽  
Sorting Algorithms ◽  
Key Technologies ◽  
Simulation Results ◽  
Sequence Simulation

The real-time radar signal sorting is one of the key technologies for electronic reconnaissance, first analyzes the defects of traditional main sorting algorithms, and then proposes a comprehensive main sorting algorithm. The method first uses the SDIF algorithm to sort PRI fixed and PRI stagger radar signal, then uses dynamic expansion association method to search PRI jitter radar signal. When using the SDIF algorithm, in order to improve the efficiency of extraction of PRI, first taking amplitude pretreatment, then only accumulate the signals whose amplitude meet certain requirements. After extracting PRI, extract to the original full pulse sequence. Simulation results show that the method of sorting has high accuracy and good real-time.

A Real-time Radar Signal Sorting Method and Implementation Based on DSP

2020 ◽  
Author(s):  
Dalong Xu ◽  
Min Xu ◽  
Hao Wang ◽  
Feng Feng ◽  
Liming Tang ◽  
...  
Keyword(s):  
Real Time ◽  
Radar Signal ◽  
Sorting Method

scholarly journals A “Smart” Biosensor-Enabled Intravascular Catheter and Platform for Dynamic Delivery of Propofol to “Close the Loop” for Total Intravenous Anesthesia

2021 ◽  
Vol 186 (Supplement_1) ◽  
pp. 370-377
Author(s):  
Edward Chaum ◽  
Ernő Lindner
Keyword(s):  
Real Time ◽  
Drug Distribution ◽  
Population Based ◽  
Pharmacokinetic Data ◽  
Drug Infusion ◽  
Blood Concentrations ◽  
The Usa ◽  
The Difference ◽  
Automated Delivery

ABSTRACT Background Target-controlled infusion anesthesia is used worldwide to provide user-defined, stable, blood concentrations of propofol for sedation and anesthesia. The drug infusion is controlled by a microprocessor that uses population-based pharmacokinetic data and patient biometrics to estimate the required infusion rate to replace losses from the blood compartment due to drug distribution and metabolism. The objective of the research was to develop and validate a method to detect and quantify propofol levels in the blood, to improve the safety of propofol use, and to demonstrate a pathway for regulatory approval for its use in the USA. Methods We conceptualized and prototyped a novel “smart” biosensor-enabled intravenous catheter capable of quantifying propofol at physiologic levels in the blood, in real time. The clinical embodiment of the platform is comprised of a “smart” biosensor-enabled catheter prototype, a signal generation/detection readout display, and a driving electronics software. The biosensor was validated in vitro using a variety of electrochemical methods in both static and flow systems with biofluids, including blood. Results We present data demonstrating the experimental detection and quantification of propofol at sub-micromolar concentrations using this biosensor and method. Detection of the drug is rapid and stable with negligible biofouling due to the sensor coating. It shows a linear correlation with mass spectroscopy methods. An intuitive graphical user interface was developed to: (1) detect and quantify the propofol sensor signal, (2) determine the difference between targeted and actual propofol concentration, (3) communicate the variance in real time, and (4) use the output of the controller to drive drug delivery from an in-line syringe pump. The automated delivery and maintenance of propofol levels was demonstrated in a modeled benchtop “patient” applying the known pharmacokinetics of the drug using published algorithms. Conclusions We present a proof-of-concept and in vitro validation of accurate electrochemical quantification of propofol directly from the blood and the design and prototyping of a “smart,” indwelling, biosensor-enabled catheter and demonstrate feedback hardware and software architecture permitting accurate measurement of propofol in blood in real time. The controller platform is shown to permit autonomous, “closed-loop” delivery of the drug and maintenance of user-defined propofol levels in a dynamic flow model.

scholarly journals A Modified KNN Algorithm for High-Performance Computing on FPGA of Real-Time m-QAM Demodulators

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 627
Author(s):  
David Marquez-Viloria ◽  
Luis Castano-Londono ◽  
Neil Guerrero-Gonzalez
Keyword(s):  
Real Time ◽  
High Performance ◽  
Interference Mitigation ◽  
Parallel Implementation ◽  
Computational Time ◽  
Successful Implementation ◽  
Interchannel Interference ◽  
The Difference ◽  
High Level ◽  
Performance Computing

A methodology for scalable and concurrent real-time implementation of highly recurrent algorithms is presented and experimentally validated using the AWS-FPGA. This paper presents a parallel implementation of a KNN algorithm focused on the m-QAM demodulators using high-level synthesis for fast prototyping, parameterization, and scalability of the design. The proposed design shows the successful implementation of the KNN algorithm for interchannel interference mitigation in a 3 × 16 Gbaud 16-QAM Nyquist WDM system. Additionally, we present a modified version of the KNN algorithm in which comparisons among data symbols are reduced by identifying the closest neighbor using the rule of the 8-connected clusters used for image processing. Real-time implementation of the modified KNN on a Xilinx Virtex UltraScale+ VU9P AWS-FPGA board was compared with the results obtained in previous work using the same data from the same experimental setup but offline DSP using Matlab. The results show that the difference is negligible below FEC limit. Additionally, the modified KNN shows a reduction of operations from 43 percent to 75 percent, depending on the symbol’s position in the constellation, achieving a reduction 47.25% reduction in total computational time for 100 K input symbols processed on 20 parallel cores compared to the KNN algorithm.

The feasibility and validity of using a real time location system (RTLS) to measure bedside contact time

2021 ◽  
pp. 174498712110161
Author(s):  
Ann-Marie Cannaby ◽  
Vanda Carter ◽  
Thomas Hoe ◽  
Stephenson Strobel ◽  
Elena Ashtari Tafti ◽  
...  
Keyword(s):  
Real Time ◽  
Contact Time ◽  
Measured Data ◽  
Complete Agreement ◽  
Observation Data ◽  
The Real ◽  
Measured Time ◽  
The Difference ◽  
Hospital Wards ◽  
Location Systems

Background The association between the nurse-to-patient ratio and patient outcomes has been extensively investigated. Real time location systems have the potential capability of measuring the actual amount of bedside contact patients receive. Aims This study aimed to determine the feasibility and accuracy of real time location systems as a measure of the amount of contact time that nurses spent in the patients’ bed space. Methods An exploratory, observational, feasibility study was designed to compare the accuracy of data collection between manual observation performed by a researcher and real time location systems data capture capability. Four nurses participated in the study, which took place in 2019 on two hospital wards. They were observed by a researcher while carrying out their work activities for a total of 230 minutes. The amount of time the nurses spent in the patients’ bed space was recorded in 10-minute blocks of time and the real time location systems data were extracted for the same nurse at the time of observation. Data were then analysed for the level of agreement between the observed and the real time location systems measured data, descriptively and graphically using a kernel density and a scatter plot. Results The difference (in minutes) between researcher observed and real time location systems measured data for the 23, 10-minute observation blocks ranged from zero (complete agreement) to 5 minutes. The mean difference between the researcher observed and real time location systems time in the patients’ bed space was one minute (10% of the time). On average, real time location systems measured time in the bed space was longer than the researcher observed time. Conclusions There were good levels of agreement between researcher observation and real time location systems data of the time nurses spend at the bedside. This study confirms that it is feasible to use real time location systems as an accurate measure of the amount of time nurses spend at the patients’ bedside.

scholarly journals Time versus State in Insurance: Experimental Evidence from Contract Farming in Kenya

2018 ◽  
Vol 108 (12) ◽  
pp. 3778-3813 ◽  
Author(s):  
Lorenzo Casaburi ◽  
Jack Willis
Keyword(s):  
Crop Insurance ◽  
Counterparty Risk ◽  
Harvest Time ◽  
Contract Farming ◽  
Present Bias ◽  
The Poor ◽  
Percentage Points ◽  
Randomized Control ◽  
The Difference

The gains from insurance arise from the transfer of income across states. Yet, by requiring that the premium be paid up front, standard insurance products also transfer income across time. We show that this intertemporal transfer can help explain low insurance demand, especially among the poor, and in a randomized control trial in Kenya we test a crop insurance product which removes it. The product is interlinked with a contract farming scheme: as with other inputs, the buyer of the crop offers the insurance and deducts the premium from farmer revenues at harvest time. The take-up rate for pay-at-harvest insurance is 72 percent, compared to 5 percent for the standard pay-up-front contract, and the difference is largest among poorer farmers. Additional experiments and outcomes provide evidence on the role of liquidity constraints, present bias, and counterparty risk, and find that enabling farmers to commit to pay the premium just 1 month later increases demand by 21 percentage points. (JEL G22, I32, O13, O16, Q12, Q14)

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