Embedded system upgrade based on Raspberry Pi computer for a 23/31 GHz dual-channel water vapor radiometer

2020 ◽  
Author(s):  
Daniel Ferrusca Rodriguez ◽  
Jetzael Cuazoson ◽  
Jesús Contreras ◽  
David Hiriart ◽  
Eduardo Ibarra Medel ◽  
...  
Keyword(s):  
Water Vapor ◽  
Embedded System ◽  
Raspberry Pi ◽  
Dual Channel ◽  
Channel Water ◽  
Water Vapor Radiometer

scholarly journals Air Learning: a deep reinforcement learning gym for autonomous aerial robot visual navigation

2021 ◽  
Author(s):  
Srivatsan Krishnan ◽  
Behzad Boroujerdian ◽  
William Fu ◽  
Aleksandra Faust ◽  
Vijay Janapa Reddi
Keyword(s):  
Reinforcement Learning ◽  
Embedded System ◽  
Broad Class ◽  
Visual Navigation ◽  
Raspberry Pi ◽  
Latency Distribution ◽  
Hardware In The Loop ◽  
Resource Constrained ◽  
Aerial Robot ◽  
Policy Optimization

AbstractWe introduce Air Learning, an open-source simulator, and a gym environment for deep reinforcement learning research on resource-constrained aerial robots. Equipped with domain randomization, Air Learning exposes a UAV agent to a diverse set of challenging scenarios. We seed the toolset with point-to-point obstacle avoidance tasks in three different environments and Deep Q Networks (DQN) and Proximal Policy Optimization (PPO) trainers. Air Learning assesses the policies’ performance under various quality-of-flight (QoF) metrics, such as the energy consumed, endurance, and the average trajectory length, on resource-constrained embedded platforms like a Raspberry Pi. We find that the trajectories on an embedded Ras-Pi are vastly different from those predicted on a high-end desktop system, resulting in up to $$40\%$$ 40 % longer trajectories in one of the environments. To understand the source of such discrepancies, we use Air Learning to artificially degrade high-end desktop performance to mimic what happens on a low-end embedded system. We then propose a mitigation technique that uses the hardware-in-the-loop to determine the latency distribution of running the policy on the target platform (onboard compute on aerial robot). A randomly sampled latency from the latency distribution is then added as an artificial delay within the training loop. Training the policy with artificial delays allows us to minimize the hardware gap (discrepancy in the flight time metric reduced from 37.73% to 0.5%). Thus, Air Learning with hardware-in-the-loop characterizes those differences and exposes how the onboard compute’s choice affects the aerial robot’s performance. We also conduct reliability studies to assess the effect of sensor failures on the learned policies. All put together, Air Learning enables a broad class of deep RL research on UAVs. The source code is available at: https://github.com/harvard-edge/AirLearning.

RANCANG BANGUN INTERNET OF THINGS SERVER DENGAN MENGGUNAKAN ACTIVEMQ ARTEMIS UNTUK MITIGASI BENCANA DI RASPBERRY PI 3

Transmisi ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. 117-122
Author(s):  
Sadr Lufti Mufreni ◽  
Esi Putri Silmina
Keyword(s):  
Internet Of Things ◽  
Embedded System ◽  
Raspberry Pi

Indonesia merupakan negara kepulauan yang mempunyai lebih dari 13.000 pulau. Wilayahnya terletak di antara Samudera Hindia dan Samudera Pasifik dan dilewati oleh Pacific Ring of Fire sehingga banyak gunung berapi aktif. Berdasarkan letak geografis mempunyai potensi tsunami dan gempa bumi cukup tinggi. Diperlukan rencana penanggulangan bencana yang baik untuk menekan risiko yang bisa terjadi, salah satunya dengan mitigasi bencana. Mitigasi bencana adalah serangkaian upaya untuk mengurangi risiko bencana, baik melalui pembangunan fisik maupun penyadaran dan peningkatan kemampuan menghadapi ancaman bencana. Mitigasi bencana diperlukan untuk mengurangi dampak yang ditimbulkan terutama korban jiwa. Salah satunya dengan menggunakan sistem peringatan dini. Sistem peringatan dini terdiri dari 3 komponen utama yaitu sensor untuk mendapatkan nilai dari suatu lingkungan, controller untuk mengolah nilai yang diterima, dan aksi yang dilakukan berdasarkan hasil dari pengolahan. Untuk membuat sistem yang efektif diperlukan komunikasi yang memadai. Messaging queue digunakan oleh industri untuk komunikasi antar perangkat lunak, perangkat keras, dan embedded system. Penelitian berfokus pada penggunaan ActiveMQ Artemis sebagai messaging queue sebagai server untuk komunikasi dengan internet of things (IoT). Keunggulan ActiveMQ Artemis dapat dijalankan di Raspberry Pi 3 dengan sedikit modifikasi. Hasil penelitian membuktikan bahwa ActiveMQ Artemis dapat digunakan untuk komunikasi IoT pada simulasi sistem mitigasi bencana.

The Seoul Water Vapor Radiometer for the Middle Atmosphere: Calibration, Retrieval, and Validation

2011 ◽  
Vol 49 (3) ◽  
pp. 1052-1062 ◽  
Author(s):  
Evelyn De Wachter ◽  
Alexander Haefele ◽  
Niklaus Kampfer ◽  
Soohyun Ka ◽  
Jung Eun Lee ◽  
...  
Keyword(s):  
Water Vapor ◽  
Middle Atmosphere ◽  
Water Vapor Radiometer

scholarly journals PEMODELAN SISTEM DETEKSI WAJAH SEBAGAI PENGHITUNG JUMLAH PENUMPANG TRANSPORTASI PUBLIK

2021 ◽  
Vol 4 (1) ◽  
pp. 67-77
Author(s):  
Fransiska Sisilia Mukti ◽  
Lia Farokhah ◽  
Nur Lailatul Aqromi
Keyword(s):  
Embedded System ◽  
System Design ◽  
Face Detection ◽  
Public Transportation ◽  
Detection System ◽  
Raspberry Pi ◽  
The Internet ◽  
Detection Mechanism ◽  
Information System Design ◽  
The Face

Bus is one of public transportation and as the most preferable by Indonesian to support their mobility. The high number of bus traffics then demands the bus management to provide the maximum service for their passenger, in order to gain public trust. Unfortunately, in the reality passenger list’s fraud is often faced by the bus management, there is a mismatch list between the amount of deposits made by bus driver and the number of passengers carried by the bus, and as the result it caused big loss for the Bus management. Automatic Passenger Counting (APC) then as an artificial intelligence program that is considered to cope with the bus management problems. This research carried out an APC technology based on passenger face detection using the Viola-Jones method, which is integrated with an embedded system based on the Internet of Things in the processing and data transmission. To detect passenger images, a webcam is provided that is connected to the Raspberry pi which is then sent to the server via the Internet to be displayed on the website provided. The system database will be updated within a certain period of time, or according to the stop of the bus (the system can be adjusted according to management needs). The system will calculate the number of passengers automatically; the bus management can export passenger data whenever as they want. There are 3 main points in the architecture of modeling system, they are information system design, device architecture design, and face detection mechanism design to calculate the number of passengers. A system design test is carried out to assess the suitability of the system being built with company needs. Then, based on the questionnaire distributed to the respondent, averagely 85.12 % claim that the Face detection system is suitability. The score attained from 4 main aspects including interactivity, aesthetics, layout and personalization

scholarly journals Development of a high-stability water vapor radiometer

Radio Science ◽  
1998 ◽  
Vol 33 (2) ◽  
pp. 449-462 ◽  
Author(s):  
Alan B. Tanner
Keyword(s):  
Water Vapor ◽  
High Stability ◽  
Water Vapor Radiometer

Pointed water vapor radiometer corrections for accurate global positioning system surveying

1993 ◽  
Vol 20 (23) ◽  
pp. 2635-2638 ◽  
Author(s):  
Randolph Ware ◽  
Christian Rocken ◽  
Fredrick Solheim ◽  
Teresa Van Hove ◽  
Chris Alber ◽  
...  
Keyword(s):  
Water Vapor ◽  
Global Positioning System ◽  
Positioning System ◽  
Global Positioning ◽  
Water Vapor Radiometer

scholarly journals Methods of Correction for the “Wet” Atmosphere in Estimating Baseline Lengths from VLBI

1988 ◽  
Vol 129 ◽  
pp. 543-544
Author(s):  
G. Elgered ◽  
J. L. Davis ◽  
T. A. Herring ◽  
I. I. Shapiro
Keyword(s):  
Water Vapor ◽  
Propagation Delay ◽  
Propagation Path ◽  
Baseline Length ◽  
Space Observatory ◽  
Owens Valley ◽  
Use Of Data ◽  
Onsala Space Observatory ◽  
Vlbi Data ◽  
Water Vapor Radiometer

The error in VLBI estimates of baseline length caused by unmodelled variations in the propagation path through the atmosphere is greater for longer baselines. We present and discuss series of estimates of baseline lengths obtained using different methods to correct for the propagation delay caused by atmospheric water vapor. The main methods are use of data from a water-vapor radiometer (WVR) and Kalman-filtering of the VLBI data themselves to estimate the propagation delay. Since the longest timespan of WVR data associated with geodetic VLBI experiments was obtained at the Onsala Space Observatory in Sweden, we present results for the following three baselines: (1) Onsala–Wettzell, FRG (920 km), (2) Onsala–Haystack/Westford, MA (5600 km), and (3) Onsala–Owens Valley (7914 km).

scholarly journals Proposal for an Embedded System Architecture Using a GNDVI Algorithm to Support UAV-Based Agrochemical Spraying

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5397 ◽  
Author(s):  
Maik Basso ◽  
Diego Stocchero ◽  
Renato Ventura Bayan Henriques ◽  
André Luis Vian ◽  
Christian Bredemeier ◽  
...  
Keyword(s):  
Control System ◽  
Embedded System ◽  
Precision Agriculture ◽  
System Architecture ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Raspberry Pi ◽  
Research Station ◽  
High Definition ◽  
Cpu Usage

An important area in precision agriculture is related to the efficient use of chemicals applied onto fields. Efforts have been made to diminish their use, aiming at cost reduction and fewer chemical residues in the final agricultural products. The use of unmanned aerial vehicles (UAVs) presents itself as an attractive and cheap alternative for spraying pesticides and fertilizers compared to conventional mass spraying performed by ordinary manned aircraft. Besides being cheaper than manned aircraft, small UAVs are capable of performing fine-grained instead of the mass spraying. Observing this improved method, this paper reports the design of an embedded real-time UAV spraying control system supported by onboard image processing. The proposal uses a normalized difference vegetation index (NDVI) algorithm to detect the exact locations in which the chemicals are needed. Using this information, the automated spraying control system performs punctual applications while the UAV navigates over the crops. The system architecture is designed to run on low-cost hardware, which demands an efficient NDVI algorithm. The experiments were conducted using Raspberry Pi 3 as the embedded hardware. First, experiments in a laboratory were conducted in which the algorithm was proved to be correct and efficient. Then, field tests in real conditions were conducted for validation purposes. These validation tests were performed in an agronomic research station with the Raspberry hardware integrated into a UAV flying over a field of crops. The average CPU usage was about 20% while memory consumption was about 70 MB for high definition images, with 4% CPU usage and 20.3 MB RAM being observed for low-resolution images. The average current measured to execute the proposed algorithm was 0.11 A. The obtained results prove that the proposed solution is efficient in terms of processing and energy consumption when used in embedded hardware and provides measurements which are coherent with the commercial GreenSeeker equipment.

A Steerable Dual-Channel Microwave Radiometer for Measurement of Water Vapor and Liquid in the Troposphere

1983 ◽  
Vol 22 (5) ◽  
pp. 789-806 ◽  
Author(s):  
D. C. Hogg ◽  
F. O. Guiraud ◽  
J. B. Snider ◽  
M. T. Decker ◽  
E. R. Westwater
Keyword(s):  
Water Vapor ◽  
Microwave Radiometer ◽  
Dual Channel
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