NASA Automated Rendezvous and Capture Review. Executive summary

In support of the Cargo Transfer Vehicle (CTV) Definition Studies in FY-92, the Advanced Program Development division of the Office of Space Flight at NASA Headquarters conducted an evaluation and review of the United States capabilities and state-of-the-art in Automated Rendezvous and Capture (AR&C). This review was held in Williamsburg, Virginia on 19-21 Nov. 1991 and included over 120 attendees from U.S. government organizations, industries, and universities. One hundred abstracts were submitted to the organizing committee for consideration. Forty-two were selected for presentation. The review was structured to include five technical sessions. Forty-two papers addressed topics in the five categories below: (1) hardware systems and components; (2) software systems; (3) integrated systems; (4) operations; and (5) supporting infrastructure

Interference Path Loss Prediction in A319/320 Airplanes Using Modulated Fuzzy Logic and Neural Networks

In this paper, neural network (NN) modeling is combined with fuzzy logic to estimate Interference Path Loss measurements on Airbus 319 and 320 airplanes. Interference patterns inside the aircraft are classified and predicted based on the locations of the doors, windows, aircraft structures and the communication/navigation system-of-concern. Modeled results are compared with measured data. Combining fuzzy logic and NN modeling is shown to improve estimates of measured data over estimates obtained with NN alone. A plan is proposed to enhance the modeling for better prediction of electromagnetic coupling problems inside aircraft

Development of Low Cost Heart Rate Monitoring Device and Classification Technique Using Fuzzy Logics Algorithm

Heart as one of necessary organs, has been examined profoundly by the heart rate changes. The heart rate is affected by many factors, such as age, gender, physiological conditions. Hence, better diagnosis can be made if the interpretation of heart rate signal would be automated that eliminates the human error while comprising the influential factors. Subjective readings may lead to imprecise diagnosis. In this project, proposed tool is designed for medical experts that can reliably interpret the heart signal based on age, gender and heart condition. PPG sensor was utilized to sense the heartbeats. Furthermore, the raw signal was transferred through wireless medium using RF Transceivers and Arduino Uno as a microcontroller to the remote base station. This would let end users (physicians/Caregivers) to have a real-time heart rate monitoring without a need of connecting wires from the patient ward/room to the remote station which was designed in MATLAB GUI. The classification of the signal being obtained is achieved through fuzzy logics algorithm inside the MATLAB Fuzzy Logic Toolbox. The cost-effectiveness of the proposed project was another benefits that could be added to an automated heart rate monitoring device

An Overview on Application of Machine Learning Techniques in Optical Networks

Today's telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users' behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, Machine Learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management. The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing and compensation of nonlinear effects in optical fiber propagation. In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude the paper proposing new possible research directions

Deep Space Network information system architecture study

The purpose of this article is to describe an architecture for the Deep Space Network (DSN) information system in the years 2000-2010 and to provide guidelines for its evolution during the 1990s. The study scope is defined to be from the front-end areas at the antennas to the end users (spacecraft teams, principal investigators, archival storage systems, and non-NASA partners). The architectural vision provides guidance for major DSN implementation efforts during the next decade. A strong motivation for the study is an expected dramatic improvement in information-systems technologies, such as the following: computer processing, automation technology (including knowledge-based systems), networking and data transport, software and hardware engineering, and human-interface technology. The proposed Ground Information System has the following major features: unified architecture from the front-end area to the end user; open-systems standards to achieve interoperability; DSN production of level 0 data; delivery of level 0 data from the Deep Space Communications Complex, if desired; dedicated telemetry processors for each receiver; security against unauthorized access and errors; and highly automated monitor and control

eVentos 2 - Autonomous sailboat control

Dissertação para obtenção do Grau de Mestre em Engenharia Electrotécnica e de ComputadoresSailboat navigation started as a way to explore the world. Even though performance is significantly lower than that of a motorboat, in terms of resources, these vessels still are the best low-cost solutions. On the past, navigation depended greatly on estimates or on the stars. Nowadays it depends on precise data provided by a variety of electronic devices, independent from the user’s location. Autonomous sailboats are vessels that use only the wind for propulsion and have the capacity to control its sails and rudders without human intervention. These particularities give them almost unlimited autonomy and a very valuable ability to fulfill long term missions on the sea, such as collecting oceanographic data, search and rescue or surveillance. This dissertation presents a fuzzy logic controller for autonomous sailboats based on a proposed set of sensors, namely a GPS receiver, a weather meter and an electronic compass. Following a basic navigation approach, the proposed set of sensorswas studied in order to obtain an effective group of variables for the controller’s fuzzy sets, and rules for its rule base. In the end, four fuzzy logic controllers were designed, one for the sail(to maximize speed) and three for the rudder (in order to comply with all navigation situations). The result is a sailboat control system capable of operation in a low cost platform such as an Arduino prototyping board. Simulated results obtained from a data set of approximately 100 tests to each controller back up the theory presented for the controller’s operation, since physical experimentation was not possible

Prediction of interference Pathloss Inside Commercial Aircraft Using Modulated Fuzzy Logic and Neural Networks

Although several modeling techniques have been used to model indoor radio wave propagation and coupling patterns, to date no efficient model exists that calculates indoor-outdoor radio wave propagations on commercial aircraft. Due to the complexity of an aircraft structure, with the additive introduction of creeping wave phenomenon and unknown back-door propagation values from the exterior aircraft antenna to the avionics bay, numerical modeling approaches using Method of Moments (MoM) or Finite Difference Time Domain (FDTD) prove too complex with limitations. This dissertation presents an expert neuro-fuzzy (NF) model for Interference pathloss (IPL) predictions inside an Airbus 320 (A320) airplane, for radio systems from 75 to 1585 MHz. This novel model generates IPL pattern through fuzzy logic, incorporating linear expert knowledge into the patterns. The model also uses feed-forward neural networks to derive meanings from complicated or imprecise data, extract patterns and detect trends in the IPL data that are too complex to be noticed by either humans or other computer techniques. Unlike previous approaches, the model presented is robust in incorporating both low to high band frequencies. It is also computationally efficient and reliable

A Comparative and Analytical Review of Iot-Enabled Smart Accidental Management Systems

One of the most important issues that emerging nations are addressing is road accidents. It is important to develop smart accidental management systems with low cost and efforts to prevent accidents and causalities. The amalgamation of Intelligent Transportation Systems (ITS) and Information and Communications Technology (ICT) is expected to dramatically change how people experience driving by enabling cutting-edge traffic monitoring and incident detection strategies. This analysis focuses on various components of SAMS, such as sensor networks, communication protocols, data processing techniques, and decision-making algorithms. It examines how these components work together to create a connected infrastructure capable of detecting and responding to accidents promptly. The review highlights the role of data analytics in enhancing accident prediction and prevention. By processing and analyzing enormous real-time data from cameras, sensors, and other sources, IoT-driven SAMS can identify patterns and anomalies, allowing for proactive measures to avoid accidents in various settings, including transportation, industries, and public spaces

Events Recognition System for Water Treatment Works

The supply of drinking water in sufficient quantity and required quality is a challenging task for water companies. Tackling this task successfully depends largely on ensuring a continuous high quality level of water treatment at Water Treatment Works (WTW). Therefore, processes at WTWs are highly automated and controlled. A reliable and rapid detection of faulty sensor data and failure events at WTWs processes is of prime importance for its efficient and effective operation. Therefore, the vast majority of WTWs operated in the UK make use of event detection systems that automatically generate alarms after the detection of abnormal behaviour on observed signals to ensure an early detection of WTW’s process failures. Event detection systems usually deployed at WTWs apply thresholds to the monitored signals for the recognition of WTW’s faulty processes. The research work described in this thesis investigates new methods for near real-time event detection at WTWs by the implementation of statistical process control and machine learning techniques applied for an automated near real-time recognition of failure events at WTWs processes. The resulting novel Hybrid CUSUM Event Recognition System (HC-ERS) makes use of new online sensor data validation and pre-processing techniques and utilises two distinct detection methodologies: first for fault detection on individual signals and second for the recognition of faulty processes and events at WTWs. The fault detection methodology automatically detects abnormal behaviour of observed water quality parameters in near real-time using the data of the corresponding sensors that is online validated and pre-processed. The methodology utilises CUSUM control charts to predict the presence of faults by tracking the variation of each signal individually to identify abnormal shifts in its mean. The basic CUSUM methodology was refined by investigating optimised interdependent parameters for each signal individually. The combined predictions of CUSUM fault detection on individual signals serves the basis for application of the second event detection methodology. The second event detection methodology automatically identifies faults at WTW’s processes respectively failure events at WTWs in near real-time, utilising the faults detected by CUSUM fault detection on individual signals beforehand. The method applies Random Forest classifiers to predict the presence of an event at WTW’s processes. All methods have been developed to be generic and generalising well across different drinking water treatment processes at WTWs. HC-ERS has proved to be effective in the detection of failure events at WTWs demonstrated by the application on real data of water quality signals with historical events from a UK’s WTWs. The methodology achieved a peak F1 value of 0.84 and generates 0.3 false alarms per week. These results demonstrate the ability of method to automatically and reliably detect failure events at WTW’s processes in near real-time and also show promise for practical application of the HC-ERS in industry. The combination of both methodologies presents a unique contribution to the field of near real-time event detection at WTW

Monitoring for Precision Agriculture using Wireless Sensor Network-A review

This paper explores the potential of WSN in the area of agriculture in India. Aiming at the sugarcane crop, a multi-parameter monitoring system is designed based on low-power ZigBee wireless communication technology for system automation and monitoring. Real time data is collected by wireless sensor nodes and transmitted to base station using zigbee. Data is received, saved and displayed at base station to achieve soil temperature, soil moisture and humidity monitoring. The data is continuously monitored at base station and if it exceeds the desired limit, a message is sent to farmer on mobile through GSM network for controlling actions. The implementation of system software and hardware are given, including the design of wireless node and the implementation principle of data transmission and communication modules. This system overcomes the limitations of wired sensor networks and has the advantage of flexible networking for monitoring equipment, convenient installation and removing of equipment, low cost and reliable nodes and high capacity