A Real-Time Weed Mapping and Precision Herbicide Spraying System for Row Crops

This study developed and field tested an automated weed mapping and variable-rate herbicide spraying (VRHS) system for row crops. Weed detection was performed through a machine vision sub-system that used a custom threshold segmentation method, an improved particle swarm optimum (IPSO) algorithm, capable of segmenting the field images. The VRHS system also used a lateral histogram-based algorithm for fast extraction of weed maps. This was the basis for determining real-time herbicide application rates. The central processor of the VRHS system had high logic operation capacity, compared to the conventional controller-based systems. Custom developed monitoring system allowed real-time visualization of the spraying system functionalities. Integrated system performance was then evaluated through field experiments. The IPSO successfully segmented weeds within corn crop at seedling growth stage and reduced segmentation error rates to 0.1% from 7.1% of traditional particle swarm optimization algorithm. IPSO processing speed was 0.026 s/frame. The weed detection to chemical actuation response time of integrated system was 1.562 s. Overall, VRHS system met the real-time data processing and actuation requirements for its use in practical weed management applications.

Download Full-text

Real-Time Data Logging and Online Curve Fitting Using Raspberry Pi in Physics Laboratories

International Journal of Distance Education Technologies ◽

10.4018/ijdet.2020070104 ◽

2020 ◽

Vol 18 (3) ◽

pp. 57-77

Author(s):

Wing-Kwong Wong ◽

Kai-Ping Chen ◽

Jia-Wei Lin

Keyword(s):

Experimental Data ◽

Real Time ◽

Curve Fitting ◽

Empirical Studies ◽

Error Rates ◽

Raspberry Pi ◽

Data Logging ◽

Time Data ◽

Real Time Data ◽

Physics Laboratories

The results of PISA 2015 indicate that Taiwanese students have excellent mathematical and scientific knowledge but are weak in applying such knowledge and in conducting practical experiments in the laboratory. To support students conducting practical experiments in physics laboratories, a real-time data logging system and an online tool for fitting experimental data were developed. During data logging in an experiment, the data was immediately plotted, which enabled students to observe the characteristics of the plot. The online curve fitting system, which employed Internet of Things technologies, allowed students to fit experimental data to various mathematical functions and plot a function curve superimposed on the data. Two empirical studies were conducted involving first-year university students and secondary school teachers. The results indicated that these developed tools improved students' understanding of an experiment's mathematical characteristics. The average curve fitting error rates of students and teachers were 4.62% and 1.4%, respectively.

Download Full-text

Planning for LOTOS: A New LOwer Troposphere Observing System

10.5194/egusphere-egu2020-19600 ◽

2020 ◽

Author(s):

Terry Hock ◽

Tammy Weckwerth ◽

Steve Oncley ◽

William Brown ◽

Vanda Grubišić ◽

...

Keyword(s):

Boundary Layer ◽

Real Time ◽

Sensor Network ◽

Regional Scale ◽

Lower Troposphere ◽

Integrated System ◽

Lower Atmosphere ◽

Time Data ◽

Wide Range ◽

Real Time Data

The National Center for Atmospheric Research Earth Observing Laboratory (EOL) proposes to develop the LOwer Troposphere Observing System (LOTOS), a new integrated sensor network that offers the potential for transformative understanding of the lower atmosphere and its coupling to the Earth's surface.&#160;&#160;The LOTOS sensor network is designed to allow simultaneous and coordinated sampling both vertically, through the atmospheric planetary boundary layer, and horizontally, across the surrounding landscape, focusing on the land-atmosphere interface and its coupling with the overlying free troposphere. The core of LOTOS will be a portable integrated network of up to five nodes, each consisting of a profiling suite of instruments surrounded by up to fifteen flux measuring towers. LOTOS will provide an integrated set of measurements needed to address outstanding scientific challenges related to processes within the atmospheric surface layer, boundary layer, and lower troposphere. LOTOS will also enable novel quantification of exchanges of biogeochemical and climate-relevant gases from microscale up to regional scale.&#160;&#160;LOTOS&#8217; uniqueness lies in its ability to simultaneously sample both horizontally and vertically as an integrated system, but also in its flexibility to be easily relocated as a portable field-deployable system suitable for addressing a wide range of research needs. LOTOS will provide real-time data quality control, combine measurements from a variety of sensors into integrated data products, and provide real-time data displays. It is envisioned that LOTOS will become part of the deployable NSF Lower Atmosphere Observing Facilities (LAOF) and thus be available to a broad base of NSF users from both observational and modeling communities. LOTOS offers the potential for transformative understanding of the Earth and its atmosphere as a coupled system. This presentation will describe the background, motivation, plan, and timeline for the LOTOS&#8217; proposed development.

Download Full-text

Early Detection of Broad-Leaved and Grass Weeds in Wide Row Crops Using Artificial Neural Networks and UAV Imagery

Agronomy ◽

10.3390/agronomy11040749 ◽

2021 ◽

Vol 11 (4) ◽

pp. 749

Author(s):

Jorge Torres-Sánchez ◽

Francisco Javier Mesas-Carrascosa ◽

Francisco Jiménez-Brenes ◽

Ana de Castro ◽

Francisca López-Granados

Keyword(s):

Neural Networks ◽

Classification Problems ◽

Weed Detection ◽

Row Crops ◽

Weed Mapping ◽

Herbicide Treatment ◽

Herbaceous Crops ◽

Aerial Platforms ◽

Grass Weed

Significant advances in weed mapping from unmanned aerial platforms have been achieved in recent years. The detection of weed location has made possible the generation of site specific weed treatments to reduce the use of herbicides according to weed cover maps. However, the characterization of weed infestations should not be limited to the location of weed stands, but should also be able to distinguish the types of weeds to allow the best possible choice of herbicide treatment to be applied. A first step in this direction should be the discrimination between broad-leaved (dicotyledonous) and grass (monocotyledonous) weeds. Considering the advances in weed detection based on images acquired by unmanned aerial vehicles, and the ability of neural networks to solve hard classification problems in remote sensing, these technologies have been merged in this study with the aim of exploring their potential for broadleaf and grass weed detection in wide-row herbaceous crops such as sunflower and cotton. Overall accuracies of around 80% were obtained in both crops, with user accuracy for broad-leaved and grass weeds around 75% and 65%, respectively. These results confirm the potential of the presented combination of technologies for improving the characterization of different weed infestations, which would allow the generation of timely and adequate herbicide treatment maps according to groups of weeds.

Download Full-text

An Integrated System for Drilling Real Time Data Analytics

10.2118/181001-ms ◽

2016 ◽

Cited By ~ 5

Author(s):

Ahmed M. Alsalama ◽

Joseph P. Canlas ◽

Salem H. Gharbi

Keyword(s):

Real Time ◽

Data Analytics ◽

Integrated System ◽

Time Data ◽

Real Time Data

Download Full-text

A Wireless Network for Collection and Synthesis of Mobile Mesoscale Weather Observations

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech1678.1 ◽

2004 ◽

Vol 21 (11) ◽

pp. 1659-1670 ◽

Cited By ~ 7

Author(s):

Conrad L. Ziegler ◽

Douglas Kennedy ◽

Erik N. Rasmussen

Keyword(s):

Real Time ◽

Wireless Network ◽

Field Experiments ◽

The Internet ◽

Field Observations ◽

Time Data ◽

Digital Network ◽

Weather Observations ◽

Instrumented Vehicles ◽

Gathering Data

Abstract This paper reports the development of a wireless network of instrumented vehicles and aircraft for the real-time collection and synthesis of their mobile weather observations in mesoscale field experiments. The mobile digital network (MDN) utilizes 900-MHz radio frequency modem technology, enabling real-time data transmissions at up to 115 kbit s−1 across a domain of about 40 km on a side. The effective throughput of the network of multiple mobile units is about 40 kbit s−1 due to overhead from data quality checking and acknowledgment that data have been received. After gathering data from mobile observing platforms at a centrally located mobile command post, both image products and data are then uplinked via geostationary satellite at about 80 kbit s−1 and served to the Internet. The first application of the MDN was to mobile field observations obtained during the International H2O Project (IHOP).

Download Full-text

INTERNET OF THINGS BASED REAL-TIME VITAL SIGN MONITORING SYSTEM USING MOBILE APPLICATION

Istrazivanja i projektovanja za privredu ◽

10.5937/jaes0-28774 ◽

2021 ◽

pp. 1-7

Author(s):

Sugondo Hadiyoso ◽

Akhmad Alfaruq ◽

Rohmat Tulloh ◽

Yuyun Siti Rohmah ◽

Erwin Susanto

Keyword(s):

Internet Of Things ◽

Real Time ◽

Monitoring System ◽

Vital Sign ◽

Integrated System ◽

Easy Access ◽

The Internet ◽

Real Time Monitoring ◽

Time Data ◽

Average Delay

The development of telehealth technology in monitoring systems has been widely used to support applications in the health sector. The aim is to provide easy access for the community. One of the implications is a real-time monitoring system based on the Internet of Things (IoT) platform. Some health vital signs that are the focus of observation are ECG signal, SpO2, blood pressure and Heart rate which can provide heart health information. In this study an integrated system has been implemented, namely Vital Sign distributed monitoring system through the internet network. The implemented system was able to acquire vital sign then send data to the internet cloud to be stored and processed further for real-time monitoring needs by interested parties. An Android based application that was developed called iHealth VitalSign monitor capable of sending, processing and representing data in numerical and graphical forms. The average delay for each packet delivery was 154.73 ms and conform with the ITU-T recommendations for real-time data transfer. HR detection algorithms have been evaluated on real-time ECG signals, more than 2100 beats were tested and obtained an average accuracy of 98.78%. With this proposed application, it is hoped that it can increase the penetration of telehealth services.

Download Full-text

Real-Time Upper-Ocean Temperature Observations from Aircraft during Operational Hurricane Reconnaissance Missions: AXBT Demonstration Project Year One Results

Weather and Forecasting ◽

10.1175/waf-d-12-00107.1 ◽

2013 ◽

Vol 28 (6) ◽

pp. 1404-1422 ◽

Cited By ~ 11

Author(s):

Elizabeth R. Sanabia ◽

Bradford S. Barrett ◽

Peter G. Black ◽

Sue Chen ◽

James A. Cummings

Keyword(s):

Real Time ◽

Field Experiments ◽

Demonstration Project ◽

Intensity Change ◽

Upper Ocean ◽

Naval Research ◽

Ocean Temperature ◽

Time Data ◽

Forecast Models ◽

Temperature Observations

Abstract Thousands of aircraft observations of upper-ocean thermal structures have been obtained during hurricane and typhoon research field experiments in recent decades. The results from these experiments suggest a strong correlation between upper-ocean thermal variability and tropical cyclone (TC) intensity change. In response to these results, during the Office of the Federal Coordinator of Meteorology (OFCM) 2011 Interdepartmental Hurricane Conference (IHC), the Working Group for Hurricane and Winter Storms Operations and Research (WG/HWSOR) approved a 3-yr project to demonstrate the usefulness of airborne expendable bathythermographs (AXBTs) in an operational setting. The goal of this project was to initialize and validate coupled TC forecast models and was extended to improve input to statistical intensity forecast models. During the first season of the demonstration project, 109 AXBTs were deployed between 28 July and 28 August 2011. Successes included AXBT deployment from WC-130J aircraft during operational reconnaissance missions tasked by the National Hurricane Center (NHC), real-time onboard and postflight data processing, real-time data transmission to U.S. Navy and NOAA hurricane numerical prediction centers, and near-real-time assimilation of upper-ocean temperature observations into the Naval Research Laboratory Coupled Ocean–Atmosphere Mesoscale Prediction System-Tropical Cyclones (COAMPS-TC) forecast model. Initial results showed 1) increased model accuracy in upper-ocean temperatures, 2) minor improvements in TC track forecasts, and 3) minor improvements in TC intensity forecasts in both coupled dynamical and statistical models [COAMPS-TC and the Statistical Hurricane Intensity Prediction Scheme (SHIPS), respectively].

Download Full-text

Fast Drilling Optimizer for Drilling Automation

10.2118/200881-ms ◽

2021 ◽

Author(s):

John Abish Giftson Joy ◽

Robello Samuel

Keyword(s):

Real Time ◽

Field Data ◽

Particle Swarm Optimization Algorithm ◽

Particle Swarm ◽

Optimal Solution ◽

Solution Space ◽

Time Data ◽

Swarm Optimization ◽

The Given ◽

Drilling Time

Abstract The rate of penetration (ROP) was optimized using a particle swarm optimization algorithm for real-time field data to reduce drilling time and increase efficiency. ROP is directly related to drilling costs and is a major factor in determining mechanical specific energy, which is often used to quantify drilling efficiency. Optimization of ROP can therefore help cut down costs associated with drilling. ROP values were chosen from real-time field data, accounting for weight on bit, bit rotation, flow rate variation along with bit wear. A random forest regressor was used to find correlations between the dependent parameters. The parameters were then optimized for the given constraints to find the optimal solution space. The boundary constraints for the ROP function were determined from the real-time data. The function parameters were optimized using a particle swarm optimization algorithm. This is a meta-heuristic model used to optimize an objective function for its maximum or minimum within given constraints. The optimization method makes use of a population of solution particles which act as the particle swarm. These particles move collectively in the given solution space controlled by a mathematical model based on their position and velocity. This model makes use of the best-known solution for each particle and the global best position of the system to guide the swarm towards the optimal solution. The function was optimized for each well, providing optimal ROP values during real-time drilling. A fast drilling optimizer is crucial to automate and streamline the drilling process. This simultaneous optimization of ROP based on real-time data can be implemented during the process thereby increasing the efficiency of drilling as well as reducing the required drilling time.

Download Full-text

Lessons Learned from the Real-World Deployment of a Connected Vehicle Testbed

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198118799034 ◽

2018 ◽

Vol 2672 (22) ◽

pp. 10-23 ◽

Cited By ~ 11

Author(s):

Mashrur Chowdhury ◽

Mizanur Rahman ◽

Anjan Rayamajhi ◽

Sakib Mahmud Khan ◽

Mhafuzul Islam ◽

...

Keyword(s):

Real Time ◽

Real World ◽

Field Experiments ◽

Data Dissemination ◽

Heterogeneous Wireless Networks ◽

Lessons Learned ◽

Connected Vehicle ◽

Time Data ◽

Data Archiving ◽

The Real

The connected vehicle (CV) system promises unprecedented safety, mobility, environmental, economic, and social benefits, which can be unlocked using the enormous amount of data shared between vehicles and infrastructure (e.g., traffic signals, centers). Real-world CV deployments, including pilot deployments, help solve technical issues and observe potential benefits, both of which support the broader adoption of the CV system. This study focused on the Clemson University Connected Vehicle Testbed (CU-CVT) with the goal of sharing the lessons learned from the CU-CVT deployment. The motivation of this study was to enhance early CV deployments with the objective of depicting the lessons learned from the CU-CVT testbed, which includes unique features to support multiple CV applications running simultaneously. The lessons learned in the CU-CVT testbed are described at three different levels: (i) the development of system architecture and prototyping in a controlled environment, (ii) the deployment of the CU-CVT testbed, and (iii) the validation of the CV application experiments in the CU-CVT. Field experiments with CV applications validated the functionalities needed for running multiple diverse CV applications simultaneously using heterogeneous wireless networking, and meeting real-time and non-real-time application requirements. The unique deployment experiences, related to heterogeneous wireless networks, real-time data aggregation, data dissemination and processing using a broker system, and data archiving with big data management tools, gained from the CU-CVT testbed, could be used to advance CV research and guide public and private agencies for the deployment of CVs in the real world.

Download Full-text

Ambulatory Assessment

European Psychologist ◽

10.1027/1016-9040.14.2.109 ◽

2009 ◽

Vol 14 (2) ◽

pp. 109-119 ◽

Cited By ~ 66

Author(s):

Ulrich W. Ebner-Priemer ◽

Timothy J. Trull

Keyword(s):

Real Time ◽

Clinical Psychology ◽

Real Life ◽

Ambulatory Assessment ◽

Computer Assisted ◽

Affective Valence ◽

Time Data ◽

Physiological Processes ◽

Context Specific ◽

Questionnaire Studies

Convergent experimental data, autobiographical studies, and investigations on daily life have all demonstrated that gathering information retrospectively is a highly dubious methodology. Retrospection is subject to multiple systematic distortions (i.e., affective valence effect, mood congruent memory effect, duration neglect; peak end rule) as it is based on (often biased) storage and recollection of memories of the original experience or the behavior that are of interest. The method of choice to circumvent these biases is the use of electronic diaries to collect self-reported symptoms, behaviors, or physiological processes in real time. Different terms have been used for this kind of methodology: ambulatory assessment, ecological momentary assessment, experience sampling method, and real-time data capture. Even though the terms differ, they have in common the use of computer-assisted methodology to assess self-reported symptoms, behaviors, or physiological processes, while the participant undergoes normal daily activities. In this review we discuss the main features and advantages of ambulatory assessment regarding clinical psychology and psychiatry: (a) the use of realtime assessment to circumvent biased recollection, (b) assessment in real life to enhance generalizability, (c) repeated assessment to investigate within person processes, (d) multimodal assessment, including psychological, physiological and behavioral data, (e) the opportunity to assess and investigate context-specific relationships, and (f) the possibility of giving feedback in real time. Using prototypic examples from the literature of clinical psychology and psychiatry, we demonstrate that ambulatory assessment can answer specific research questions better than laboratory or questionnaire studies.

Download Full-text