Hardware-software Platform Research Articles

AEcroscopy: A Software-Hardware Framework Empowering Microscopy Toward Automated and Autonomous Experimentation.

Microscopy has been pivotal in improving the understanding of structure-function relationships at the nanoscale and is by now ubiquitous in most characterization labs. However, traditional microscopy operations are still limited largely by a human-centric click-and-go paradigm utilizing vendor-provided software, which limits the scope, utility, efficiency, effectiveness, and at times reproducibility of microscopy experiments. Here, a coupled software-hardware platform is developed that consists of a software package termed AEcroscopy (short for Automated Experiments in Microscopy), along with a field-programmable-gate-array device with LabView-built customized acquisition scripts, which overcome these limitations and provide the necessary abstractions toward full automation of microscopy platforms. The platform works across multiple vendor devices on scanning probe microscopes and electron microscopes. It enables customized scan trajectories, processing functions that can be triggered locally or remotely on processing servers, user-defined excitation waveforms, standardization of data models, and completely seamless operation through simple Python commands to enable a plethora of microscopy experiments to be performed in a reproducible, automated manner. This platform can be readily coupled with existing machine-learning libraries and simulations, to provide automated decision-making and active theory-experiment optimization to turn microscopes from characterization tools to instruments capable of autonomous model refinement and physics discovery.

A Deep Learning-Based Platform for Workers' Stress Detection Using Minimally Intrusive Multisensory Devices.

The advent of Industry 4.0 necessitates substantial interaction between humans and machines, presenting new challenges when it comes to evaluating the stress levels of workers who operate in increasingly intricate work environments. Undoubtedly, work-related stress exerts a significant influence on individuals' overall stress levels, leading to enduring health issues and adverse impacts on their quality of life. Although psychological questionnaires have traditionally been employed to assess stress, they lack the capability to monitor stress levels in real-time or on an ongoing basis, thus making it arduous to identify the causes and demanding aspects of work. To surmount this limitation, an effective solution lies in the analysis of physiological signals that can be continuously measured through wearable or ambient sensors. Previous studies in this field have mainly focused on stress assessment through intrusive wearable systems susceptible to noise and artifacts that degrade performance. One of our recently published papers presented a wearable and ambient hardware-software platform that is minimally intrusive, able to detect human stress without hindering normal work activities, and slightly susceptible to artifacts due to movements. A limitation of this system is its not very high performance in terms of the accuracy of detecting multiple stress levels; therefore, in this work, the focus was on improving the software performance of the platform, using a deep learning approach. To this purpose, three neural networks were implemented, and the best performance was achieved by the 1D-convolutional neural network with an accuracy of 95.38% for the identification of two levels of stress, which is a significant improvement over those obtained previously.

Indoor Air Quality Analysis Using Recurrent Neural Networks: A Case Study of Environmental Variables

In the pursuit of energy efficiency and reduced environmental impact, adequate ventilation in enclosed spaces is essential. This study presents a hybrid neural network model designed for monitoring and prediction of environmental variables. The system comprises two phases: An IoT hardware–software platform for data acquisition and decision-making and a hybrid model combining short-term memory and convolutional recurrent structures. The results are promising and hold potential for integration into parallel processing AI architectures.

NeoCam: An Edge-Cloud Platform for Non-Invasive Real-Time Monitoring in Neonatal Intensive Care Units.

In this work we introduce NeoCam, an open source hardware-software platform for video-based monitoring of preterms infants in Neonatal Intensive Care Units (NICUs). NeoCam includes an edge computing device that performs video acquisition and processing in real-time. Compared to other proposed solutions, it has the advantage of handling data more efficiently by performing most of the processing on the device, including proper anonymisation for better compliance with privacy regulations. In addition, it allows to perform various video analysis tasks of clinical interest in parallel at speeds of between 20 and 30 frames-per-second. We introduce algorithms to measure without contact the breathing rate, motor activity, body pose and emotional status of the infants. For breathing rate, our system shows good agreement with existing methods provided there is sufficient light and proper imaging conditions. Models for motor activity and stress detection are new to the best of our knowledge. NeoCam has been tested on preterms in the NICU of the University Hospital Puerta del Mar (Cádiz, Spain), and we report the lessons learned from this trial.

Multi-Sensor Platform for Predictive Air Quality Monitoring.

Air quality monitoring is a very important aspect of providing safe indoor conditions, and carbon dioxide (CO2) is one of the pollutants that most affects people's health. An automatic system able to accurately forecast CO2 concentration can prevent a sudden rise in CO2 levels through appropriate control of heating, ventilation and air-conditioning (HVAC) systems, avoiding energy waste and ensuring people's comfort. There are several works in the literature dedicated to air quality assessment and control of HVAC systems; the performance maximisation of such systems is typically achieved using a significant amount of data collected over a long period of time (even months) to train the algorithm. This can be costly and may not respond to a real scenario where the habits of the house occupants or the environment conditions may change over time. To address this problem, an adaptive hardware-software platform was developed, following the IoT paradigm, with a high level of accuracy in forecasting CO2 trends by analysing only a limited window of recent data. The system was tested considering a real case study in a residential room used for smart working and physical exercise; the parameters analysed were the occupants' physical activity, temperature, humidity and CO2 in the room. Three deep-learning algorithms were evaluated, and the best result was obtained with the Long Short-Term Memory network, which features a Root Mean Square Error of about 10 ppm with a training period of 10 days.

Модуль для проверки плат семейства Arduino

The article highlights the results of developing an electronic module for testing Arduino boards. The hardware and software platform mentioned is often used for educational purposes to study microcontroller systems and robotic devices. The ability of testing the board before reusing it helps troubleshoot the electronic device. In the course of the study electrical structural and circuit diagrams of the module were developed, and a special test program was created. The module implements software and hardware diagnostic methods, which allows testing with high precision and ensuring the necessary reliability of robotic devices developed on its basis. To create a diagnostic module there were identified the nodes of the Arduino hardware-software platform with a high failure rate, an input-output line check node, an ADC and power system check node, an EEPROM check node and a power supply with a fuse. A practical value of the development lies in the fact that the test program is loaded directly into the microcontroller of the tested board allowing to quickly and accurately test I / O lines with a high failure rate. Nowadays, there are no such devices for diagnosing malfunctions of the Arduino hardware and software platforms. Using the Arduino software and hardware platform in educational processes opens the new opportunities for both students and schoolchildren. Projects implemented in secondary professional technical institutions motivate students to acquire new knowledge, develop interest in technology, programming and design, develop logical and algorithmic thinking.

Human Postures Recognition by Accelerometer Sensor and ML Architecture Integrated in Embedded Platforms: Benchmarking and Performance Evaluation

Embedded hardware systems, such as wearable devices, are widely used for health status monitoring of ageing people to improve their well-being. In this context, it becomes increasingly important to develop portable, easy-to-use, compact, and energy-efficient hardware-software platforms, to enhance the level of usability and promote their deployment. With this purpose an automatic tri-axial accelerometer-based system for postural recognition has been developed, useful in detecting potential inappropriate behavioral habits for the elderly. Systems in the literature and on the market for this type of analysis mostly use personal computers with high computing resources, which are not easily portable and have high power consumption. To overcome these limitations, a real-time posture recognition Machine Learning algorithm was developed and optimized that could perform highly on platforms with low computational capacity and power consumption. The software was integrated and tested on two low-cost embedded platform (Raspberry Pi 4 and Odroid N2+). The experimentation stage was performed on various Machine Learning pre-trained classifiers using data of seven elderly users. The preliminary results showed an activity classification accuracy of about 98% for the four analyzed postures (Standing, Sitting, Bending, and Lying down), with similar accuracy and a computational load as the state-of-the-art classifiers running on personal computers.

Platforms for Optogenetic Stimulation and Feedback Control

Harnessing the potential of optogenetics in biology requires methodologies from different disciplines ranging from biology, to mechatronics engineering, to control engineering. Light stimulation of a synthetic optogenetic construct in a given biological species can only be achieved via a suitable light stimulation platform. Emerging optogenetic applications entail a consistent, reproducible, and regulated delivery of light adapted to the application requirement. In this review, we explore the evolution of light-induction hardware-software platforms from simple illumination set-ups to sophisticated microscopy, microtiter plate and bioreactor designs, and discuss their respective advantages and disadvantages. Here, we examine design approaches followed in performing optogenetic experiments spanning different cell types and culture volumes, with induction capabilities ranging from single cell stimulation to entire cell culture illumination. The development of automated measurement and stimulation schemes on these platforms has enabled researchers to implement various in silico feedback control strategies to achieve computer-controlled living systems—a theme we briefly discuss in the last part of this review.

Hardware-Software Platform for the Development of STEM Skills

Robotics not only play a significant role in medicine, manufacturing, or aerospace industry, but also in education. According to governmental studies carried out in the United States and Europe, careers related to STEM skills like engineering, are constantly increasing worldwide. Due to this, several countries have been implementing STEM education. But, in Colombia this initiative just recently started to take place. Currently, it is possible to find several STEM educational robotic kits on the market such as boost from Lego, Cozmo from Anki, Mbot from Makeblocks and Dash from Make wonder. In this trend, the STEM robotic kit proposed in this document, pretends to be a hardware grounding to implement a whole STEAM program. It intends to encourage children to develop their science, tech, math, and artistic outcomes. Also, the kit offers a differential plus from others because it has two interactive components: an elephant-bot called Eli, a robot easy to assemble inspired in the protection of African elephants; and, an intuitive user interface, called “Experiencia Chamali” made up of three modules. The first one consists of assembly the robot, the second one is designed for developing of spatial perception throughout maze challenges, and the last one is made for test the robot’s movements live. Two groups of children between 6 and 11 years old tested the robotic platform. During the activity, the groups reinforced STEM skills such as teamwork, communication, and problem-solving as a conclusion of test results. The children divided the assembly of the elephant-bot into several tasks on their initiative to make an agile construction. Also, in the challenge of the labyrinth, they worked collaboratively on the spatial location. Finally, the members of each group worked as a team decorating the physical labyrinth freely and observing the movements of the “elephant-bot” in this environment.

СПОСОБ СОЗДАНИЯ ДОВЕРЕННОЙ АППАРАТНО-ПРОГРАММНОЙ ПЛАТФОРМЫ ДЛЯ ПРИМЕНЕНИЯ В ИНФОРМАЦИОННЫХ СИСТЕМАХ СПЕЦИАЛЬНОГО НАЗНАЧЕНИЯ

The paper presents a method to increase trust levels of foreign and domestic-made hardware-software platforms, designed to build specialised devices and computing facilities, which are meeting safety requirements and protected from BIOS vulnerabilities, to work with classified information. The purpose of the study is to investigate the ability of designing trusted foreign and domestic-made hardware-software platforms, protected from exploiting BIOS vulnerabilities. Research methods: in order to choose PC module that will be used for designing trusted hardware-software platform an analysis of Russian industrial-grade PC modules was made. In addition an analysis of known BIOS vulnerabilities was made. Proprietary BIOS replacement in a form of domestic-made Horizon bootloader, which includes unauthorised access to information protection measures was made as well as a possibility of practical use of trusted hardware-software platform with Horizon bootloader was overviewed. Obtained result: PC module for trusted hardware-software platform was selected, proprietary BIOS replacement in a form of domestic-made Horizon bootloader, which includes unauthorized access to information protection measures, was made; an increase in the level of trust levels of foreign and domestic-made hardware-software platforms, which are used to create specialized devices and computing facilities while meeting safety requirements and protected from BIOS vulnerabilities to work with classified information has been ensured; an approach to create trusted hardware-software platform design requirements and conditions was proposed; needs to exclude potentially dangerous Intel Management Engine controller's functionality were justified and proposal to use trusted hardware-software platform with Horizon bootloader was justified.

Recovering Information from Wireless Sensors in Hardware and Software Platforms

The work considers the task of information processing in a subsystem of the hardware-software platform of the simulator complex - a mobile system of simulating isolation breathing apparatuses. The problem of predicting values when data packets are lost during their wireless transmission has been revealed. To solve the problem, an algorithm for data processing based on neural network technology has been developed, which allows reducing the number of data packet losses by predicting the lost values. The experimental studies confirmed the adequacy and effectiveness of the proposed algorithm. The use of neural networks in solving the problems of information processing has improved the accuracy of this process.

Do You Like What You (Can't) See? The Differential Effects of Hardware and Software Upgrades on High-Tech Product Evaluations

“Over-the-air” software upgrades are a recent trend in the automotive industry to distribute firmware directly to customers via the Internet. These upgrades can endow the vehicle with completely new functions that enhance safety, convenience, and performance. The US manufacturer Tesla was the first to integrate such upgrades on a regular basis to let customers participate in continuous innovation, giving Tesla a competitive edge over rival brands. Since then, several premium manufacturers have followed suit. This research examines how consumers perceive and respond to software upgradeability in durable high-tech products vis-à-vis integral product alternatives and hardware upgradeability. Results of four studies with more than 3,000 participants suggest that consumers evaluate software upgrades less positive than hardware upgrades because they perceive software as less capable to enhance product quality. However, bundling software upgrades to larger packages and stressing the low effort required to integrate new functions are viable ways to remedy this perception. Importantly, the findings suggest that offering products fully equipped with all premium functions to be unlocked via software upgrades—as done by several premium brands—is a risky strategy. Consumers react negatively to the artificial restriction of functionalities, irrespective of whether these are offered as temporary leases or permanent purchases. The findings provide initial evidence for the game-changing potential of software upgradeability and offer managers concrete guidance for designing and marketing upgradeable hardware-software platforms.

Повышение уровня доверия к аппаратно-программным платформам с целью предупреждения компьютерных атак из-за уязвимостей в по BIOS

Abstract: in this publicaton, a technique to increase trust levels of foreign and domestic-made hardware-software platforms, which are used to create specialised devices and computing facilities, which are meeting safety requirements and protected from BIOS vulnerabilities, to work with classified information, was made. Problems, which developer might encounter, were listed; methods of troubleshooting were proposed, and conclusions were made. The purpose of research is to investigate an ability of designing trusted foreign and domestic-made hardwaresoftware platforms, protected from exploiting BIOS vulnerabilities. Research methods: in order to achieve the purpose of research, an analysis of Russian’s industrial-grade PC modules was made in order to choose PC module that will be used for designing trusted hardware-software platform, an analysis of known BIOS vulnerabilities was made; proprietary BIOS replacement in a form of domestic-made Horizon bootloader, which includes unauthorised access to information protection measures, was made and possibilty of practical use of trusted hardware-software platform with Horizon bootloader was overviewed. Obtained result: PC module for trusted hardware-software platform was selected, proprietary BIOS replacement in a form of domestic-made Horizon bootloader, which includes unauthorized access to information protection measures, was made; technique to increase trust levels of foreign and domestic-made hardware-software platforms, which are used to create specialized devices and computing facilities, which are meeting safety requirements and protected from BIOS vulnerabilities, to work with classified information, was made; an approach to create trusted hardware-software platform design requirements and conditions was made; needs to exclude potentially dangerous Intel Management Engine controller’s functionality were justified and proposal to use trusted hardware-software platform with Horizon bootloader was made.

Office and industrial information systems and networks in the training of modern specialists

Article discusses an interdisciplinary optional course “Office and industrial information systems and networks”. Course provides skills in preparing of hardware-software platform formation of a workplace. This course is developed for students whose field of study is not related to information technologies, but whose professional activities will be carried out in the modern enterprises in the context of Industry 4.0 development.

Associated Reality: A cognitive Human–Machine Layer for autonomous driving

Advanced Driver Assistance Systems (ADAS) and Automated and Autonomous Vehicles (AV) are cooperative systems and processes that use: artificial intelligence, cognitive methods, cloud technologies, cooperative vehicle-to-everything-communications (V2X), software–hardwareplatforms, sensor platforms and incipient intelligent transport infrastructures, to get self-driving systems and smart connected mobility services. This paper, to support automated driving systems (assisted, semi-autonomous and fully autonomous vehicles), introduces a cognitive layer called Associated Reality to enhance the involved information, knowledge and communication processes. The architecture defined includes an augmented Local Dynamic Map, with complementary layers, and an augmented Graph Database, with complementary semantic–cognitive relations, for the considered purpose, in cooperative human–machine and machine–machine systems. Virtual augmented landmarks are defined to improve the connectivity and intelligence of the involved spatial-information systems. Different structure landmarks and sequence landmarks (which includes regular, repetitive and periodic landmarks) are defined, categorized and used in diverse visual localization and mapping scenarios, for autonomous driving. In this paper, it is also shown, as a proof-of-concept for vehicle localization and mapping in road tunnels, the visual detection of different sequences of periodic luminaires, using YOLO v3 for the corresponding LED lights detection, or a specific alternative procedure developed with very low computational cost.

Virtualization Management Concept for Flexible and Fault-Tolerant Smart Grid Service Provision

In modern power systems, reliable provision of grid services (e.g., primary and ancillary services) are highly dependent on automation systems in order to have monitoring, processing, decision making and communication capabilities. The operational flexibility of automation systems is essential for the reliable operation of power systems during and after disruptive events. However, this is restricted by integrated hardware-software platforms. Therefore, it will be difficult to reconfigure control strategies during run time. This paper presents the concept of Grid Function Virtualization (GFV) as a potential approach to improve the operational flexibility of grid automation systems. GFV has been proposed to offer a new way to deploy and manage grid services by leveraging virtualization technology. The main idea of GFV is to run grid services (i.e., software implementation of services) independently from underlying hardware. To realize the important design considerations, the GFV architecture and its building blocks is elaborated in details. To this end, an exhaustive review of applications of virtualization in several domains is provided to show the importance of virtualization in improving flexibility and resource utilization. Finally, the advantages of the proposed concept to deal with disruptions in power systems is demonstrated in a proof of concept based on a CIGRE MV benchmark grid.

MicrIO: an open-source autosampler and fraction collector for automated microfluidic input-output.

Microfluidic devices are an enabling technology for many labs, facilitating a wide range of applications spanning high-throughput encapsulation, molecular separations, and long-term cell culture. In many cases, however, their utility is limited by a 'world-to-chip' barrier that makes it difficult to serially interface samples with these devices. As a result, many researchers are forced to rely on low-throughput, manual approaches for managing device input and output (IO) of samples, reagents, and effluent. Here, we present a hardware-software platform for automated microfluidic IO (micrIO). The platform, which is uniquely compatible with positive-pressure microfluidics, comprises an 'AutoSipper' for input and a 'Fraction Collector' for output. To facilitate widespread adoption, both are open-source builds constructed from components that are readily purchased online or fabricated from included design files. The software control library, written in Python, allows the platform to be integrated with existing experimental setups and to coordinate IO with other functions such as valve actuation and assay imaging. We demonstrate these capabilities by coupling both the AutoSipper and Fraction Collector to two microfluidic devices: a simple, valved inlet manifold and a microfluidic droplet generator that produces beads with distinct spectral codes. Analysis of the collected materials in each case establishes the ability of the platform to draw from and output to specific wells of multiwell plates with negligible cross-contamination between samples.

On the Stability of a Hardware Compensation Mechanism for Embedded Energy Harvesting Emulators

The possibility of emulating renewable energy sources by means of portable, low-cost embedded devices is a key factor for the design and validation of ultra low-power networked embedded systems. Full characterisation of hardware-software platforms used for reliably and adaptively generating energy traces is therefore needed in order to clearly understand their adoption for testing energy harvesting devices or protocols. We investigate in this study a recently proposed embedded ultra-low power solution, which targets energy harvesting sources emulation with real-time responsiveness. The analyzed platform has been previously evaluated in terms of accuracy and reactiveness. However, given the presence of a positive feedback mechanism implemented by means of a compensation circuit, the possibility of unstable dynamics could hinder its applicability. It is therefore deemed interesting to delineate the conditions which guarantee the stability of the system. The aim of this article is to investigate the problem, to formally derive the electrical loads to be powered that allow for operate in a stable regime, and to experimentally assess properties in realistic scenarios. Theoretical and experimental results highlight the flexibility of the analyzed platform in terms of its capability to quickly adapt to changes in load conditions, while retaining bounded output dynamics.

HackRF + GNU Radio: A software-defined radio to teach communication theory

In this paper, an alternative to the traditional methodology related to signal processing-like subjects is proposed. These are subjects that require a deep mathematical and theoretical basis, but the practical goal is not often emphasized, which drives students to lose interest in the subject. Thus, a software-defined radio environment is proposed to provide a more practical view of the subject. This solution consists of an open hardware–software platform able to capture and process a wide range of frequencies. HackRF is the hardware component, while GNU Radio will provide the graphical support to this device. The tests performed with a set of 36 students have revealed that they are more satisfied with this framework than just employing a traditional equation-based environment as Matlab. Furthermore, their scores in the exams also support the suitability of the proposed platform.

Digital Decommissioning Software-Hardware Package – Tool for Design and Management of the Decommissioning of Nuclear Facilities

The main directions for adopting information technologies at the concluding stage of the life cycle of nuclear facilities for the purpose of automating and increasing the efficiency of decommissioning work are examined. A fundamentally new software-hardware platform – Digital Decommissioning – is presented; it combines a complex of software-hardware tools making it possible to reach a qualitatively new level of design and management of decommissioning. The basic functional blocks and an enlarged scheme of the software-hardware complex with a description of the key characteristics of the automated work sites are listed. The purpose, makeup, and prospects for adopting the Digital Decommissioning softwarehardware package combining innovative approaches to the final stage of the life cycle of nuclear facilities are examined.