Commercial Off-the-shelf Research Articles

Identification of position, size and dynamic heat power of chips embedded in a real electronic board

Temperature is a critical factor in the lifespan of electronic systems. Therefore, it is essential to control all thermal parameters. In certain applications, such as commercial off-the-shelf (COTS) components, manufacturers may not have precise knowledge of the composition of electronic boards. As a result, it is important for them to define the positions of heat-dissipating elements, and the power dissipated to better predict the final thermal behavior of the system. Some electronic boards today even have components integrated into their volume. Since components can now be located not only on the surface but also within the volume of electronic boards, manufacturers need to define volume-based methods for detecting these heat sources. In this article, we address the problem of detecting sources within a volume using surface temperature data. To achieve this goal, we develop a representative numerical model of the system that can be used for an inverse source identification procedure. In a first time, we present a numerical approach that demonstrates the feasibility of the identification procedure on a board containing several electronic chips. Secondly, the volume source identification method is applied to a real case, using surfaces temperatures measured by infrared cameras. The experimental results obtained are consistent and show that this technique is suitable for detecting volumetric sources within an electronic board.

A Review of Techniques for Ageing Detection and Monitoring on Embedded Systems

Embedded digital devices are progressively deployed in dependable or safety-critical systems. These devices undergo significant hardware ageing, particularly in harsh environments. This increases their likelihood of failure. It is crucial to understand ageing processes and to detect hardware degradation early for guaranteeing system dependability. In this survey, we review the core ageing mechanisms, and identify and categorize general working principles of ageing detection and monitoring techniques for Commercial-Off-the-Shelf (COTS) components that are prevalent in embedded systems: Field Programmable Gate Arrays (FPGAs), microcontrollers, Systems-on-Chips (SoCs), and their power supplies. From our review, we find that online techniques are more widely applied on FPGAs than on other components, and see a rising trend towards machine learning application for analysing hardware ageing. Based on the reviewed literature, we identify research opportunities and potential directions of interest in the field. With this work, we intend to facilitate future research by systematically presenting all main approaches in a concise way.

AudioMove: Applying the Spatial Audio to Multi-Directional Limb Exercise Guidance

Guiding users with limb exercise can assist in muscle training or physical recovery. However, traditional vision-based methods often require multiple camera angles to help users understand the motions and require them to be within the range of the screen. Therefore, we propose a non-visual system that can guide users with multiple-directional limb motions utilizing spatial audio, AudioMove , with commercial-off-the-shelf (COTS) devices (i.e., smartphones and earphones). The proposed system addresses the challenge of conveying directional information encompassing multiple planes in real-time. We conduct a mixed-method user study to evaluate the effectiveness of the system with three methods combining motion data with spatial audio perception. Additionally, a user interface is built to collect users' comments. The results conclude that spatial audio guidance could create a natural, pervasive, and non-visual exercise training solution in daily life.

Total Ionizing Dose and Single-Event Effect Response of the AD524CDZ Instrumentation Amplifier

This manuscript focuses on studying the radiation response of the Commercial-off-the-shelf (COTS) AD524CDZ operational amplifier. Total Ionizing Dose (TID) effects were tested using low-dose 60Co irradiation. Single-Event Effect (SEE) sensitivity was studied on this operational amplifier using a 105 MeV proton beam. Additionally, further study of the SEE response was carried out using a Two-photon absorption laser to scan some sensitive sectors of the die. For this laser experiment, different gain setups and laser energies were employed to determine how the Single Event Transient (SET) response of the device was affected based on the test configuration. The results from the TID experiments revealed that the studied device remained functional after 100 krads (Si). Proton experiments revealed the studied device exhibited a high SET response with a maximum DC offset SET of about 1.5 V. Laser experiments demonstrated that there was a clear SET reduction when using 10× and 1000× gain setups.

Hardware Development and Evaluation of Multihop Cluster-Based Agricultural IoT Based on Bluetooth Low-Energy and LoRa Communication Technologies.

In this paper, we present the development and evaluation of a contextually relevant, cost-effective, multihop cluster-based agricultural Internet of Things (MCA-IoT) network. This network utilizes commercial off-the-shelf (COTS) Bluetooth Low-Energy (BLE) and LoRa communication technologies, along with the Raspberry Pi 3 Model B+ (RPi 3 B+), to address the challenges of climate change-induced global food insecurity in smart farming applications. Employing the lean engineering design approach, we initially implemented a centralized cluster-based agricultural IoT (CA-IoT) hardware testbed incorporating BLE, RPi 3 B+, STEMMA soil moisture sensors, UM25 m, and LoPy low-power Wi-Fi modules. This system was subsequently adapted and refined to assess the performance of the MCA-IoT network. This study offers a comprehensive reference on the novel, location-independent MCA-IoT technology, including detailed design and deployment insights for the agricultural IoT (Agri-IoT) community. The proposed solution demonstrated favorable performance in indoor and outdoor environments, particularly in water-stressed regions of Northern Ghana. Performance evaluations revealed that the MCA-IoT technology is easy to deploy and manage by users with limited expertise, is location-independent, robust, energy-efficient for battery operation, and scalable in terms of task and size, thereby providing a versatile range of measurements for future applications. Our results further demonstrated that the most effective approach to utilizing existing IoT-based communication technologies within a typical farming context in sub-Saharan Africa is to integrate them.

Transfer learning-accelerated network slice management for next generation services

The current trend in user services places an ever-growing demand for higher data rates, near-real-time latencies, and near-perfect quality of service. To meet such demands, fundamental changes were made to the traditional radio access network (RAN), introducing Open RAN (O-RAN). This new paradigm is based on a virtualized and intelligent RAN architecture. However, with the increased complexity of 5G applications, traditional application-specific placement techniques have reached a bottleneck. Our paper presents a Transfer Learning (TL) augmented Reinforcement Learning (RL) based networking slicing (NS) solution targeting more effective placement and improving downtime for prolonged slice deployments. To achieve this, we propose an approach based on creating a robust and dynamic repository of specialized RL agents and network slices geared towards popular user service types such as eMBB, URLLC, and mMTC. The proposed solution consists of a heuristic-controlled two-module-based ML Engine and a repository. The objective function is formulated to minimize the downtime incurred by the VNFs hosted on the commercial-off-the-shelf (COTS) servers. The performance of the proposed system is evaluated compared to traditional approaches using industry-standard 5G traffic datasets. The evaluation results show that the proposed solution consistently achieves lower downtime than the traditional algorithms.

From Do-It-Yourself Design to Discovery: A Comprehensive Approach to Hyperspectral Imaging from Drones

This paper presents an innovative, holistic, and comprehensive approach to drone-based imaging spectroscopy based on a small, cost-effective, and lightweight Unmanned Aerial Vehicle (UAV) payload intended for remote sensing applications. The payload comprises a push-broom imaging spectrometer built in-house with readily available Commercial Off-The-Shelf (COTS) components. This approach encompasses the entire process related to drone-based imaging spectroscopy, ranging from payload design, field operation, and data processing to the extraction of scientific data products from the collected data. This work focuses on generating directly georeferenced imaging spectroscopy datacubes using a Do-It-Yourself (DIY) imaging spectrometer, which is based on COTS components and freely available software and methods. The goal is to generate a remote sensing reflectance datacube that is suitable for retrieving chlorophyll-A (Chl-A) distributions as well as other properties of the ocean spectra. Direct georeferencing accuracy is determined by comparing landmarks in the directly georeferenced datacube to their true location. The quality of the remote sensing reflectance datacube is investigated by comparing the Chl-A distribution on various days with in situ measurements and satellite data products.

Reinforcement learning-based automated modulation switching algorithm for an enhanced underwater acoustic communication

Acoustic communication is the preferred method for underwater communication due to its superior propagation characteristics, including longer range, lower attenuation, better penetration, and adaptability to water density. However, challenges like varying water depths, temperature gradients, noise, and multipath propagation require innovative solutions beyond fixed-data rate systems and single modulation schemes. To address these challenges, we propose a reinforcement learning (RL)-based automated modulation switching algorithm designed to enhance data transmission efficiency. This approach leverages the UNETStack software and the Frequency Hopping-Binary Frequency Shift Key (FH-BFSK) modulation, extending its capabilities by incorporating Amplitude Shift Keying (ASK), Phase Shift Keying (PSK), and Orthogonal Frequency Division Multiplexing (OFDM) modulation techniques. We designed a cost-effective, software-controlled acoustic modem using commercial off-the-shelf (COTS) components, enabling full-duplex underwater communication. The RL algorithm utilizes a Q-matrix guided by a greedy policy to assess network conditions, such as data volume and data rates. It continuously monitors underwater environments to select the most suitable modulation scheme dynamically. Experimental validation demonstrates a 3.648 % improvement in Received Signal Strength Indicator (RSSI), a 32 % reduction in bit error rate at a constant 7 dB Signal-to-Noise Ratio (SNR), and a 5 % increase in utility at 10 dB SNR when using OFDM selected by the reinforcement learning algorithm, compared to FH-BFSK.

GrainSense: A Wireless Grain Moisture Sensing System Based on Wi-Fi Signals

Grain moisture sensing plays a critical role in ensuring grain quality and reducing grain losses. However, existing commercial off-the-shelf (COTS) grain moisture sensing systems are either expensive, inconvenient or inaccurate, which greatly limit their widespread deployment in real-world scenarios. To fill this gap, we develop a system called GrainSense which leverages COTS Wi-Fi devices to detect the grain moisture without the need for dedicated sensors. Specifically, we propose a wireless grain moisture detection model based on the refraction phenomenon of Wi-Fi signals and the Multiple-Input-Multiple-Output (MIMO) technology. On one hand, we correlate the grain moisture with the phase difference between two refracted Wi-Fi signals that propagate along different paths, based on which grain moisture can be deduced accordingly. On the other hand, to reduce the multi-path interference in indoor environments (e.g., the granary), we adopt Wi-Fi beamforming to enhance the refracted signal. In particular, a new signal feature (i.e., the Wi-Fi CSI beamforming ratio) is designed to eliminate the effect of sub-carrier frequency bias and cumulative phase bias. To validate the effectiveness of the developed system, we conduct extensive experiments with different types of grains in both the laboratory and the granary. Results show that the system can accurately estimate the grain moisture with an mean absolute error smaller than 5%, which meets the requirements for commercial usage. To the best of our knowledge, this is the first model-based work that achieves accurate grain moisture detection based on wireless sensing.

Off-the-shelf UHF RFID-based sensors for corrosion characterization of coated steel

The application of the UHF RFID technique in non-destructive testing (NDT) and structural health monitoring (SHM) has gained increasing attention due to its wireless, battery-less, and cost-effective attributes. It offers a promising approach for SHM and the Internet of Things (IoTs). This paper reports commercial off-the-shelf (COTS) flexible UHF RFID tag-based sensors for corrosion characterization on coated mild steel. Two types of COTS flexible UHF RFID tags with different bends are fabricated as sensors. The received signal strength indicator (RSSI) measurement is implemented to characterize the corrosion. Three parts (T-match area, dipole arms, and dipole loadings) of the two tags are tested for sensing purposes, and comparison and discussion of sensitivity, read range, and results are provided. This study successfully validates the feasibility of the proposed tag-bent method for corrosion characterization undercoating. It can be concluded that the dipole arm part of the applied COTS tags is the most sensitive area.

Technical and economic feasibility of a small vertical axis wind turbine in low wind conditions compared to other power sources for pumping water

In a global context, the significance of transitioning to renewable energy sources is paramount for sustainable development. This relevance is particularly evident in Brazil, where significant strides have been made in microgeneration of photovoltaic solar power and on-shore large-scale horizontal-axis wind power. Despite the country's extensive energy capacity from hydroelectric facilities, still approximately 38.5 million people reside in rural areas and surrounding areas, emphasizing the need for effective and affordable energy solutions for these regions. Based on this social and business opportunity of small-sized devices used in sustainable development, this practical experimentation explores the viability of initial studies from a prototyped small vertical-axis wind turbine (VAWT) system for water pumping, comparing its technical and economic feasibility with commercial off-the-shelf (COTS) photovoltaic solar pump (PV) and conventional electrical pump systems (EP). Two scenarios are investigated: (1) a patented transmission mechanism connected to a double effect pump and (2) the VAWT system designed to be connected to the same mechanism. Compared to PV ‘systems, scenario (1) offers 1.4x higher flowrate at a significantly lower cost (137% less) and consumes half the power. It offers a lower flowrate (2.6x) than EP systems but at a lower cost (36% less) and consumes one-third the power. Scenario (2) provides 2.7x less water flow than PV at a slightly lower cost (41% less). While less efficient in water flow (10 x) compared to EP, it has a slightly higher cost (15% more). This research builds upon existing knowledge and offers valuable insights for future small VAWT applications in low-wind remote areas for water pumping.

Technology modification, development, and demonstrations for future spaceflight medical systems at NASA

Throughout the history of human spaceflight, spacefarers have experienced and reported the occurrence of medical conditions, including various illnesses and injuries. Therefore, future spaceflight missions to the Moon and Mars will require the capabilities necessary for maintaining the health of these new space travelers. Mass, power, and volume available in the space vehicles used for these missions will be severely constrained. The ability to resupply or evacuate to Earth will be limited or non-existent, and ground-based support will no longer be immediate due to communication latencies and blackouts. These vehicle and mission constraints will necessitate healthcare be provided from an efficiently planned medical system. To provide the necessary care, these medical systems will need to include at a minimum, several different types of medical devices, consumable resources, centralized data management, procedural guidance, and decision support technologies. Medical devices needed for diagnosing and treating medical conditions that are expected to occur during future spaceflight missions may include real-time health monitoring, medical imaging capabilities, as well as blood and urine analysis. Novel methods for interacting with onboard patient medical records will be necessary, as will resource tracking. Terrestrial medicine shares many of these same needs, therefore a multitude of these required medical capabilities can likely be satisfied by currently available, Commercial-Off-The-Shelf (COTS) devices and methodologies; however, in some cases the unique space environment and increased mission durations will drive the need for modifications or customization of standard technologies and treatment procedures. This article will provide a review of medical devices and technologies that have been considered for inclusion within future spaceflight medical systems. It will also include a discussion about the modifications and customized development that have been performed, as well as descriptions of the technology demonstrations that have been conducted in analog and spaceflight environments.

Modelling Gd-diamond and Gd-SiC neutron detectors

A custom Monte Carlo (MC) computer model was developed to simulate thermal neutron absorption in, and subsequent photon and electron emission from, natural Gd with a view to using the material as a neutron conversion layer for neutron detectors. The MC code also modelled photon and electron detection with two dissimilar detectors: a thick (500 μm) single crystal diamond detector; and a thin (5.15 μm) commercial off the shelf (COTS) 4H–SiC photodiode detector. The detectors’ quantum detection efficiencies (QE) for hard X-rays and γ-rays were relatively low in comparison to their QE for electrons, thus making it possible to collect electron spectra from the Gd layer neutron conversion products which were not overwhelmed by photon emissions from the Gd. The MC code was utilised to determine the optimal thickness of Gd for the efficient detection of a thermal neutron flux. These radiation hard and spectroscopic detectors paired with natural Gd could find utility as robust and compact thermal neutron detectors for nuclear science and engineering, space science, and other applications.

Fly-around-the-World Near-Space Picosatellite for Cost-Effective School Space Projects

This research presents the design and development of a near-space picosatellite platform intended to operate at high altitudes between 20–40 km above sea level. These picosatellites function similarly to orbital satellites but float and travel with the wind in the atmosphere instead of orbiting the Earth. The platform utilizes a super-pressure balloon to provide buoyancy, allowing the picosatellite to remain airborne and operational for several days to months, capable of flying around the world multiple times. This study focuses on the cost-effective design of high-altitude platforms, telecommunication systems, and energy consumption for the picosatellite, enabling it to communicate with ground stations from anywhere in the world while consuming low power. The use of common COTS (Commercial Off-The-Shelf) equipment aims to enhance accessibility for educational applications. This research gathered data from our previous studies for over 20 high-altitude platform flights, analyzed the necessary factors for whole system design, and developed a new prototype that has been successfully built and tested.

High count rate alpha particle spectroscopy by a digital optical camera: A comparison study on edge detection algorithms

Alpha particle spectroscopy is highly significant in technologies related to radioactivity, particularly in radiation safety and the study of natural radioactivity. On the other hand, tools capable of identifying and determining the energies of alpha particles are of great interest. Among these tools, Commercial Off-The-Shelf (COTS) instruments, which can be used for alpha particle detection, have gained significant importance due to their ease of use and low cost. In this study, alpha particle spectroscopy of a Ra-226 source has been performed using a commercial digital camera based-on a CMOS sensor. In order to determine energy spectrum of alpha particles emitted from the source, the performance of various edge detection algorithms was examined. An algorithm based-on local pixel changes was introduced and implemented. By comparing the run times of the algorithms, it was shown that the algorithm proposed in this article exhibited the shortest run-time. Additionally, a method for rejecting pile-up events to correct their destructive effects on the alpha particle energy spectrum was introduced and implemented.

Hardness Classification Using Cost-Effective Off-the-Shelf Tactile Sensors Inspired by Mechanoreceptors

Perception is essential for robotic systems, enabling effective interaction with their surroundings through actions such as grasping and touching. Traditionally, this has relied on integrating various sensor systems, including tactile sensors, cameras, and acoustic sensors. This study leverages commercially available tactile sensors for hardness classification, drawing inspiration from the functionality of human mechanoreceptors in recognizing complex object properties during grasping tasks. Unlike previous research using customized sensors, this study focuses on cost-effective, easy-to-install, and readily deployable sensors. The approach employs a qualitative method, using Shore hardness taxonomy to select objects and evaluate the performance of commercial off-the-shelf (COTS) sensors. The analysis includes data from both individual sensors and their combinations analysed using multiple machine learning approaches, and accuracy as the primary evaluation metric was considered. The findings illustrate that increasing the number of classification classes impacts accuracy, achieving 92% in binary classification, 82% in ternary, and 80% in quaternary scenarios. Notably, the performance of commercially available tactile sensors is comparable to those reported in the literature, which range from 50% to 98% accuracy, achieving 92% accuracy with a limited data set. These results highlight the capability of COTS tactile sensors in hardness classification giving accuracy levels of 92%, while being cost-effective and easier to deploy than customized tactile sensors.

Lake Environmental Data Harvester (LED) for Alpine Lake Monitoring with Autonomous Surface Vehicles (ASVs)

This article introduces the Lake Environmental Data Harvester (LED) System, a robotic platform designed for the development of an innovative solution for monitoring remote alpine lakes. LED is intended as the first step in creating portable robotic tools that are lightweight, cost-effective, and highly reliable for monitoring remote water bodies. The LED system is based on the Shallow-Water Autonomous Multipurpose Platform (SWAMP), a groundbreaking Autonomous Surface Vehicle (ASV) originally designed for monitoring wetlands. The objective of LED is to achieve the comprehensive monitoring of remote lakes by outfitting the SWAMP with a suite of sensors, integrating an IoT infrastructure, and adhering to FAIR principles for structured data management. SWAMP’s modular design and open architecture facilitate the easy integration of payloads, while its compact size and construction with a reduced weight ensure portability. Equipped with four azimuth thrusters and a flexible hull structure, SWAMP offers a high degree of maneuverability and position-keeping ability for precise surveys in the shallow waters that are typical of remote lakes. In this project, SWAMP was equipped with a suite of sensors, including a single-beam dual-frequency echosounder, water-quality sensors, a winch for sensor deployment, and AirQino, a low-cost air quality analysis system, along with an RTK-GNSS (Global Navigation Satellite System) receiver for precise positioning. Utilizing commercial off-the-shelf (COTS) components, a Multipurpose Data-Acquisition System forms the basis for an Internet of Things (IoT) infrastructure, enabling data acquisition, storage, and long-range communication. This data-centric system design ensures that acquired variables from both sensors and the robotic platform are structured and managed according to the FAIR principles.

Ship Bridge OOW Activity Status Detection Using Wi-Fi Beamforming Feedback Information

Officers on Watch (OOWs) of the ship’s bridge play a vital role in maritime navigation safety, monitoring the ship’s navigational status, and ensuring maritime safety. The status of inactive watch officers, such as fatigued driving and negligence on lookout, is one of the main causes of accidents. Intelligent technology for real-time perception and state evaluation of ship OOWs significantly reduces accidents caused by human factors. The traditional computer vision method is difficult to adapt to the complex environment of a ship bridge, and carries strong privacy risks. With the development of Internet of Things technology, sensing technology based on ubiquitous Wi-Fi devices provides a new way to accurately monitor the status of ship OOWs. In this paper, we use commercial off-the-shelf (COTS) Wi-Fi devices to propose a ship driving activity state detection method based on beamforming feedback information (BFI). Using wireless sensing data to sense the number of OOWs and their driving behavior realizes low-cost and high-precision detection of the behavioral status of the ship’s bridge watchkeeper. Experiments were conducted in a ship-driving simulation laboratory and on a real-world Yangtze River cruise ship. The experimental results demonstrate that our proposed method achieves 92.4% and 98.1% accuracy for tracking active status and estimating the number of OOWs, respectively.

Early adverse physiological event detection using commercial wearables: challenges and opportunities

Data from commercial off-the-shelf (COTS) wearables leveraged with machine learning algorithms provide an unprecedented potential for the early detection of adverse physiological events. However, several challenges inhibit this potential, including (1) heterogeneity among and within participants that make scaling detection algorithms to a general population less precise, (2) confounders that lead to incorrect assumptions regarding a participant’s healthy state, (3) noise in the data at the sensor level that limits the sensitivity of detection algorithms, and (4) imprecision in self-reported labels that misrepresent the true data values associated with a given physiological event. The goal of this study was two-fold: (1) to characterize the performance of such algorithms in the presence of these challenges and provide insights to researchers on limitations and opportunities, and (2) to subsequently devise algorithms to address each challenge and offer insights on future opportunities for advancement. Our proposed algorithms include techniques that build on determining suitable baselines for each participant to capture important physiological changes and label correction techniques as it pertains to participant-reported identifiers. Our work is validated on potentially one of the largest datasets available, obtained with 8000+ participants and 1.3+ million hours of wearable data captured from Oura smart rings. Leveraging this extensive dataset, we achieve pre-symptomatic detection of COVID-19 with a performance receiver operator characteristic (ROC) area under the curve (AUC) of 0.725 without correction techniques, 0.739 with baseline correction, 0.740 with baseline correction and label correction on the training set, and 0.777 with baseline correction and label correction on both the training and the test set. Using the same respective paradigms, we achieve ROC AUCs of 0.919, 0.938, 0.943 and 0.994 for the detection of self-reported fever, and 0.574, 0.611, 0.601, and 0.635 for detection of self-reported shortness of breath. These techniques offer improvements across almost all metrics and events, including PR AUC, sensitivity at 75% specificity, and precision at 75% recall. The ring allows continuous monitoring for detection of event onset, and we further demonstrate an improvement in the early detection of COVID-19 from an average of 3.5 days to an average of 4.1 days before a reported positive test result.

Neutralization of ricin toxin on building interior surfaces using liquid decontaminants.

Ricin is a highly toxic protein, capable of inhibiting protein synthesis within cells, and is produced from the beans of the Ricinus communis (castor bean) plant. Numerous recent incidents involving ricin have occurred, many in the form of mailed letters resulting in both building and mail sorting facility contamination. The goal of this study was to assess the decontamination efficacy of several commercial off-the-shelf (COTS) cleaners and decontaminants (solutions of sodium hypochlorite [bleach], quaternary ammonium, sodium percarbonate, peracetic acid, and hydrogen peroxide) against a crude preparation of ricin toxin. The ricin was inoculated onto four common building materials (pine wood, drywall joint tape, countertop laminate, and industrial carpet), and the decontaminants were applied to the test coupons using a handheld sprayer. Decontamination efficacy was quantified using an in-vitro cytotoxicity assay to measure the quantity of bioactive ricin toxin extracted from test coupons as compared to the corresponding positive controls (not sprayed with decontaminant). Results showed that decontamination efficacy varied by decontaminant and substrate material, and that efficacy generally improved as the number of spray applications or contact time increased. The solutions of 0.45% peracetic acid and the 20,000-parts per million (ppm) sodium hypochlorite provided the overall best decontamination efficacy. The 0.45% peracetic acid solution achieved 97.8 to 99.8% reduction with a 30-min contact time.