AFRL plug-and-play Spacecraft Avionics Experiment (SAE)

Over the past five years, the Air Force Research Laboratory and the Space Computer Corporation have supported the Civil Air Patrol in the development of the Airborne Real-time Cueing Hyperspectral Enhanced Reconnaissance (ARCHER) system, which combines a visible/near infrared hyperspectral imaging system, high-resolution panchromatic imaging sensor, a real-time on-board target detection processor and other supporting elements to aid in civilian search-and-rescue missions. The procurement and fielding of the ARCHER system provided useful practical experience in terms of moving hyperspectral remote sensing into an operational context, including areas where further advances are needed. It also provided an airborne collection platform with which to experiment and refine target detection and change detection algorithms to gain greater insight into detection performance. Many of the system and algorithm characteristics and refinements have been reported previously in the literature. This paper attempts to go one step further in providing several of the key lessons-learned from CAP ARCHER experience, with a specific aim to illuminate the areas where further progress is needed and suggest approaches that may address these remaining shortfalls.

[Abstract] The Operationally Responsive Spa ce (ORS) program envisions building a 6 day to support fast changing tactical missions. A key requirement would be the ability to rapidly compose the spacecraft that would perform both the needed mission - and spacecraft -oriented functionality using (PnP) enabled spacecraft components. To address this need, a service -oriented spacecraft architectural model is under development as part of the Air Force Research Laboratory (AFRL) Responsive Space Testbed effort to provide a reusable , reference infrastructure for Responsive Space. The Lockheed Martin ATC is pursuing the development of a Java -based distributed architecture environment that supports this service -oriented, reference spacecraft architectural model. A key component of th is approach involves a simulation architecture that is based on spacecraft services, much like the service -oriented models now widely used in the consumer marketplace, but is an evolutionary step to extend simulation to operations. To span the entire range from simulation to operations seamlessly, a single vertically integrated software architecture is needed. The Java -based distributed architecture provides such an environment with its evolution from desktop, to enterprise, to mobile devices, and now to real time systems. The Java environment addresses the complexity needed for operational simulations and ultimate deployment for integrated spacecraft flight and payload control systems. The Real Time Specification for Java (RTSJ) supports hard real time, s oft real time and non -real time processes all interoperating within the same virtual machine. Initial prototyping is being done using IBM's Real Time Java (RTJ) implementation of the RTSJ. The Java platform support several libraries, and one in particular, the JINI protocol, which supports the operation of dynamically changing networks of distributed services (and devices). Using JINI, running as a non -real time process within IBM's RTJ environment, provides the rich set of Plug -N-Play capabilities needed to demonstrate both automatic configuration, as would be needed for Assembly, Integration and Test (AI&T), as well as for operational fault tolerance and reconfigurability needed for on -orbit operations.

This research aims to develop an Internet of Things (IoT)-based automated system for monitoring temperature and pH in the context of gourami fish farming. Gourami fish ponds are often situated in various environments with significant variations in environmental conditions. The system is designed to enable pond owners to remotely monitor real-time water temperature and pH, which are key factors in maintaining optimal water quality and fish health. The temperature sensor used is the DS18B20, while the pH sensor used is the E210C. The ESP32 platform is employed due to its integrated Wi-Fi capabilities. Monitoring displays are accessible on an LCD, personal computer (PC), and smartphone. This research involves the calibration and validation of temperature and pH sensors to ensure accurate measurements. The average standard deviation value for the temperature sensor is 0.092, and for the pH sensor, it is 0.031. The average accuracy of the temperature sensor is 98.60%, while the pH sensor has an average accuracy of 98.41%. The results demonstrate that IoT-based temperature and pH monitoring allow for in-depth data analysis of environmental conditions and long-term trend analysis in pond management.

An IoMT-based health monitoring system for coma patients ensures a secure environment by continuously tracking vital signs. It uses sensors such as heart rate, temperature, and vibration sensors to monitor the patient’s condition. The system provides real-time data, allowing healthcare professionals to respond quickly to any critical changes. This approach enhances patient safety by enabling remote monitoring. The goal is to improve care for patients who cannot communicate their needs.IOMT based health monitoring system employ wearable sensors and medical devices to continuously collect and transmit real-time physiological data from patients.This data is securely transmitted to cloud servers for storage and analysis.Healthcare providers access insights through specialized platforms,enabling remote monitoring and personalized patient care.Challenges include data security and regulatory compliance,yet these systems promise improved patient outcomes,enhanced efficiency, and grater patient engagement in healthcare management. Keywords—NodeMCU ESP32, IoT, Temperature sensor, vibration sensor, pulse sensor, GSR sensor,blynk app.

Cattle farming is one of the major branches of agriculture. People who grew and monitor cattle often don’t have just a few of them. Instead, they have many numbers of cows, horses, oxen, etc. When the number of cattle increases, it becomes tougher for the one who maintains the cattle to check up on their health individually. To resolve this issue, this study aims the development of an IoT system that is capable of monitoring the temperature and heartbeat rate of the sensor periodically. This is achieved using sensors like temperature sensors, heartbeat rate sensors, etc. It also uses software like ThingSpeak, Rasbian OS, etc. The temperature of the cattle is measured with a temperature sensor and transmitted to the raspberry pi. The heart rate sensor also sends data about the cattle's heartbeat rate to the pi. The GPS sends the cattle's latitude and longitude to the module. The information is then sent to the ThingSpeak software. The data collected by the sensors is displayed by ThingSpeak software. The data includes the temperature of the cow, the heartbeat rate of the cattle, and the latitude and longitude details of the cow.

This paper describe the importance of temperature sensor built-in self test (BIST) for microhotplate-based sensors. It shows possible ways to implement BIST functionality for microhotplate temperature sensors, including resistance temperature detectors (RTD), microhotplate thermal efficiency, and Thermocouples. A calibration technique is discussed and conclusions are given.

Midcourse Space Experiment attitude system performance for gyro disabled operations

In this paper, we presented the wireless sensor networks (WSN) to observe the human physiological signals by ZigBee, which is provided with lower power consumption, small volume, high expansion, stylization and two-way transmission, etc. ZigBee is generally used for home care, digital home control, industrial and security control. This paper developed a suite of home care sensor network system by ZigBee's characteristic, which is embedded three sensors (temperature sensor, humid sensor, and light sensor). It is attached to other sensors, such as the biosensor for observe heart rate and blood pressure. We implemented Panasonic EW3035 blood pressure monitor to measure heart rate and blood pressure. The biosensor transmits measured signals via ZigBee, and then sends to the remote wireless monitor for acquiring the observed human physiological signals. The remote wireless monitor is constructed of ZigBee and personal computer (PC). The measured signals send to the PC, which can be data collection, analyzing and statistic. When the measured signals over the standard value, the personal computer sends Global System for Mobile Communication (GSM) short message to the manager. The manager can use the PC or personal digital assistant (PDA) to observe the observed human physiological signals in the remote place.

In this paper we describe the application of a personal digital assistant (PDA) or pocket PC as an effective communication device to telemonitor levels of asthma triggering gases collected from a remote location under test to a workstation which has a personal computer (PC) running on Windows XP® as the operating system. The Bluetooth® features of the PDA are explored to transmit data collected by a Direct™ Sense Tox toxic gas monitor equipped with five toxic gas probes and one temperature sensor in real time, thereby making this telemonitoring system an innovative instrument in monitoring levels of asthma triggering gases in the El Paso-border metropolitan region, a region in which asthma is highly prevalent especially in children. At the workstation or fixed location these readings are displayed using a custom made, user friendly graphical user interface (GUI) developed using software tools like action scripting with Macromedia® Flash™. The growing advancement in technology and ever diminishing sizes of handheld devices encouraged us to opt for this configuration. Moreover, the PDA and toxic gas monitor were also chosen for their light weight, portability, flexibility, low cost and data collection and transmission capabilities.

In Malaysia, oil palm industry has made an enormous contribution to economic and social prosperity. However, it has been affected by basal stem rot (BSR) disease caused by Ganoderma boninense (G. boninense) fungus. The conventional practice to detect the disease is through manual inspection by a human expert every two weeks. This study aimed to identify the most suitable machine learning model to classify the inoculated (I) and uninoculated (U) oil palm seedlings with G. boninense before the symptoms’ appearance using hyperspectral imaging. A total of 1122 sample points were collected from frond 1 and frond 2 of 28 oil palm seedlings at the age of 10 months old, with 540 and 582 reflectance spectra extracted from U and I seedlings, respectively. The significant bands were identified based on the high separation between U and I seedlings, where the differences were observed significantly in the NIR spectrum. The reflectance values of each selected band were later used as input parameters of the 23 machine learning models developed using decision trees, discriminant analysis, logistic regression, naïve Bayes, support vector machine (SVM), k-nearest neighbor (kNN), and ensemble modelling with various types of kernels. The bands were optimized according to the classification accuracy achieved by the models. Based on the F-score and performance time, it was demonstrated that coarse Gaussian SVM with 9 bands performed better than the models with 35, 18, 14, and 11 bands. The coarse Gaussian SVM achieved an F-score of 95.21% with a performance time of 1.7124 s when run on a personal computer with an Intel® Core™ i7-8750H processor and 32 GB RAM. This early detection could lead to better management in the oil palm industry.

A method of monitoring and analysing temperature distribution in a patient's bed during bed rest and sleep is described. The system consists of 16 temperature sensors, a solid-state recorder and a personal computer. The temperature sensors are attached on the surface of a bed mat. The temperature of the 16 measurement points are recorded and stored on a multichannel solid-state recorder, where a programmable read-only memory (PROM) is used as the memory device. The PROM is detachable from the recorder, and the temperature data is read by a computer. A two-dimensional temperature distribution pattern is obtained by interpolating the temperature between measured points. The system was effective for long-term temperature monitoring without patient discomfort, and it proved reliable and easy to use. The temperature distribution and changes in the distribution indicated body movement and hence periods of sleep.

Remote health monitoring system has been an interesting topic recently among medical practitioners, engineers as well as IT professionals. However, the application of remote health monitoring system where doctor's can monitor patients' vital signs via web is practically new in Malaysia and other countries. Remote health monitoring system is beneficial to the patients and society where the implementation of such system will save hospital bill, waiting time and reduce traffics in the hospital. The objective of this project is to design and develop body temperature measurement device that can be observe by the doctor in real time as well as history data via internet with an alarm/indication in case of abnormalities. In the proposed health monitoring system, heart rate and body temperature wireless sensors were developed, however this paper only focus on body temperature wireless monitoring system. The temperature sensors will send the readings to a microcontroller using Xbee wireless communication. To send the real-time data to health monitoring database, wireless local area network (WLAN) has been used. Arduino with Ethernet shield based on IEEE 802.11 standard has been used for this purpose. Test results from a group of voluntary shows the real-time temperature reading successfully monitored locally (at home) and remotely (at doctor's computer) and the readings are comparable to commercial thermometer.

In terms of livestock monitoring systems, IoT is used to monitor the health metrics such as heart rate and temperature in real time. However, it is this same data that now provides critical data for early detection of health problem, thus enabling timely interventions and optimal practices in the management. Continuous health parameter monitoring and analysis of these health parameters have been improved considerably which resulted in the mortality rates, animal welfare and operational efficiency improved considerably. Real time data collection and processing is done by the IoT enabled livestock health monitoring system by means of heart rate and temperature sensors together with a Raspberry Pi. With a GSM/GPS module integrated with Raspberry Pi, which was connected to a GPIO, it can send data using an ESP8266 module for tracking location and cellular access. Powered by a reliable input source, the system runs constantly and sends data to the cloud where it is monitored remotely at any time through a web interface. If parameters deviate from normal ranges, real time alerts are generated to notify suitable authorities for timely health intervention and improve their livestock management. Results of simulation show a power consumption of about 5 Watts for reliable operation and constant monitoring. Accurate GPS tracking data represent livestock's spatial distribution that serves as a basis for conducting targeted health checks and ensuring optimal use of resources. The system overall improves livestock management through constant delivery of the real time health insights, enabling timely responses and by allowing stable power operation, thus saving operations costs.

With the growing emphasis on physical health monitoring, blood oxygen data has become a key indicator of overall health. Therefore, the design of oximeter is crucial for meeting people's daily health needs. Traditional single-function oximeter cannot meet people's requirements for obtaining multiple health data at the same time. Therefore, this article designs an oximeter based on STM32 microcontroller. This oximeter integrates additional functions such as heart rate detection, temperature detection, and step detection on the basis of blood oxygen measurement, and can provide more data than a simple oximeter. The system utilizes STM32 as the main control chip, MAX30102 as the heart rate and blood oxygen sensor, DS18B20 as the temperature sensor, and ADXL345 as the step detection sensor. Experimental results show that the multi-sensor oximeter provides accurate temperature, step count, and blood oxygen data, with the latter closely matching the measurements obtained from an Apple Watch, thus meeting the design requirements.

Real-time prediction of water quality is very critical to ensure the safety of the people and the continuation of aquatic life. It presents an Internet-of-Things (IoT) system, which connects pH, turbidity, flow rate, and temperature sensors with Raspberry Pi and Arduino Uno microcontroller boards to provide continuous in situ data acquisition. The raw measurements undergo a successive step of standardization by global local depth normalization, followed by the Hippopotamus Optimization Algorithm that enhances only the most informative features. The prediction is conducted using a tailored deep-learning network, the Improved Local Binary Pattern Shallow Deep Convolutional Neural Network optimized with Starling Murmuration Optimization (ILBPSDNet-SMO). The architecture produces high-accuracy water-quality predictions with near-real-time latency, which are displayed on a web dashboard and mobile application to enable quick decision-making. The proposed system is highly accurate (above 99%) and computationally efficient, as demonstrated by large-scale experiments involving Water Quality Index (WQI) annotation of drinking standards. These findings point to the possibility of using the platform as a basis for early-warning alerts and strategic planning towards sustainable management of water resources.