Low-cost Microcomputer Research Articles

The application of a low-cost microcomputer logger with pressure-temperature based sensor for coastal observations: A preliminary study

Abstract Nearshore hydrodynamics, such as water level fluctuations, field observations play a crucial role in understanding and monitoring the dynamics of the coastal region. The observed parameters provide insight into oceanographic processes, climate change impacts, and the behavior of marine ecosystems that could be valuable for coastal management and infrastructure development planning. Most of the available hydrodynamic loggers are provided by companies with relatively high prices. Due to limited budgets, many areas including Indonesia waters, have limited hydrodynamic observation data. In this paper, we presented a prototype of a simple (DIY-Do It Yourself) and low-cost water level logger with a microcomputer that could be applied in coastal regions. The system consists of a pressure-based sensor to detect water level fluctuations, a temperature sensor, a single-board microcomputer and data logger, and a power supply with different sampling frequencies for various coastal applications. The result showed the ability of the microcomputer system to measure high-resolution water levels and temperatures applicable for non-directional waves, tides and non-tides observations, and ecological monitoring. The microcomputer’s low power consumption makes it suitable for long-term coastal observations, even in remote or battery-powered applications. The body of the logger is designed from PVC-nylon with sensors made from waterproof and corrosion-free materials to ensure its applicability for coastal monitoring. Moreover, the flexibility of the microcomputer system allows for customization and adaptation to specific research requirements at relatively affordable prices.

Image-based phenotyping to estimate anthocyanin concentrations in lettuce.

Anthocyanins provide blue, red, and purple color to fruits, vegetables, and flowers. Due to their benefits for human health and aesthetic appeal, anthocyanin content in crops affects consumer preference. Rapid, low-cost, and non-destructive phenotyping of anthocyanins is not well developed. Here, we introduce the normalized difference anthocyanin index (NDAI), which is based on the optical properties of anthocyanins: high absorptance in the green and low absorptance in the red part of the spectrum. NDAI is determined as (Ired - Igreen)/(Ired + Igreen), where I is the pixel intensity, a measure of reflectance. To test NDAI, leaf discs of two red lettuce (Lactuca sativa) cultivars 'Rouxai' and 'Teodore' with wide range of anthocyanin concentrations were imaged using a multispectral imaging system and the red and green images were used to calculate NDAI. NDAI and other commonly used indices for anthocyanin quantification were evaluated by comparing to with the measured anthocyanin concentration (n = 50). Statistical results showed that NDAI has advantages over other indices in terms of prediction of anthocyanin concentrations. Canopy NDAI, obtained using multispectral canopy imaging, was correlated (n = 108, R2 = 0.73) with the anthocyanin concentrations of the top canopy layer, which is visible in the images. Comparison of canopy NDAI from multispectral images and RGB images acquired using a Linux-based microcomputer with color camera, showed similar results in the prediction of anthocyanin concentration. Thus, a low-cost microcomputer with a camera can be used to build an automated phenotyping system for anthocyanin content.

Multispectral image sensor attachable to low-cost microcomputers

Multispectral image sensor attachable to low-cost microcomputers

E-TBNet: Light Deep Neural Network for Automatic Detection of Tuberculosis with X-ray DR Imaging.

Currently, the tuberculosis (TB) detection model based on chest X-ray images has the problem of excessive reliance on hardware computing resources, high equipment performance requirements, and being harder to deploy in low-cost personal computer and embedded devices. An efficient tuberculosis detection model is proposed to achieve accurate, efficient, and stable tuberculosis screening on devices with lower hardware levels. Due to the particularity of the chest X-ray images of TB patients, there are fewer labeled data, and the deep neural network model is difficult to fully train. We first analyzed the data distribution characteristics of two public TB datasets, and found that the two-stage tuberculosis identification (first divide, then classify) is insufficient. Secondly, according to the particularity of the detection image(s), the basic residual module was optimized and improved, and this is regarded as a crucial component of this article’s network. Finally, an efficient attention mechanism was introduced, which was used to fuse the channel features. The network architecture was optimally designed and adjusted according to the correct and sufficient experimental content. In order to evaluate the performance of the network, it was compared with other lightweight networks under personal computer and Jetson Xavier embedded devices. The experimental results show that the recall rate and accuracy of the E-TBNet proposed in this paper are better than those of classic lightweight networks such as SqueezeNet and ShuffleNet, and it also has a shorter reasoning time. E-TBNet will be more advantageous to deploy on equipment with low levels of hardware.

Parallel processing proposal by clustering integration of low-cost microcomputers

Regarding parallel processing, a study brings a parallel computing approach by integrating low-cost microcomputers. In the given research, the main aspects linked to parallel processing and its motivation are exposed, widespread today by the need for data mining integrated with large-scale databases, prominent data characteristics, large volumes of data, lack of agile processing for real-time analysis, and high variability of data. For a given proposal, a case study scenario is addressed, where the database composed of more than twelve years of continuous records was processed, requiring processing and processing of the data present for the feasibility of a machine learning project. In the study, the main gains of the implemented system are presented, mainly the use of only 18% of the time required if it was considered a traditional model in the processing of the base. At the end of the article, considerations under the research and proposals for future works are presented.

History and metrology applications of a game-changing technology: digital holography [Invited

In digital holography (DH), information in the hologram is recorded and stored in digital format in discrete bits. Like its parent, holography, DH evolved over many years with periods of dormancy and revival. Almost abandoned, multiple times, unanticipated events or developments in separate industries revived it with explosive, quantum jumps, making it useful and popular to a wide audience. Although its history has been treated in many papers and books, the field is dynamic and constantly providing new opportunities. Having been born long before low-cost, fast, powerful digital computers and digital detectors were available, DH was confined to the academic world, where practical applications and commercial opportunities were few if any. Consumer demand that led to low-cost personal computers, high-resolution digital cameras, supporting software, and related products changed the situation drastically by providing every potential researcher affordable, powerful hardware and software needed to apply image processing algorithms and move DH to new practical application levels. In this paper, as part of the sixtieth anniversary of off-axis holography, we include a brief introduction to the fundamentals of DH and examine the history and evolution of DH during its periods of rise and fall. We summarize many new emerging techniques, applications, and potential future applications along with additional details for metrological examples from the authors' research.

MobileNet family tailored for Raspberry Pi

With the advances in systems-on-a-chip technologies, there is a growing demand to deploy intelligent vision systems on low-cost microcomputers. To address this challenge, much of the recent research has focused on reducing the model size and computational complexity of contemporary convolutional neural networks (CNNs). The state-of-the-art lightweight CNN is MobileNetV3. However, it was designed to achieve a good trade-off between accuracy and latency on a single large core of a Google Pixel 1 smartphone. Accordingly, MobileNetV3 is not optimized for platforms with different hardware characteristics and its predecessors may perform better for a given target platform. The aim of this paper is twofold: 1) to analyze the performance of different compact CNNs on Raspberry Pi 4; 2) to manually adapted the most promising models to better utilize the Raspberry Pi 4 hardware. After exploring a number of modifications, we present a new CNN architecture, namely MobileNetV3-Small-Pi, which is 36% faster and slightly more accurate on ImageNet classification compared to the baseline MobileNetV3-Small.

Developing a Remote Laboratory System of Stepper Motor for Learning Support

Contribution: This article proposes a low-cost and mobile device-based learning support system for the stepper motor, which provides Web-based remote laboratory. This system allows young engineers to learn about rotational principles, control methods, and programming in stepper motors. Background: Familiarity with electrical machinery, such as motors, is an important factor for electric power engineers. Hands-on laboratories are the most effective and important learning method in the study of electrical machinery. However, the Hands-on laboratories require commitments of time, space, and significant number of expensive laboratory instruments. Intended Outcomes: In the proposed system, the students are free to repeat the experiment using a real stepper motor, for example, using a smartphone. This learning support system that combines remote laboratory and virtual laboratory is expected to be an easy-to-use for young engineers. Application Design: Proposed system consists of the low-cost microcomputers, such as raspberry Pi and Arduino, and does not use expensive software, such as LabVIEW and MATLAB. It has both low cost and compatibility with any mobile devices. Findings: The learning effectiveness and usability of the system were evaluated through questionnaires and tests for a total of 50 students at the authors' department. Consequently, the results of the evaluation tests and questionnaires demonstrated that the proposed system will be useful to study the stepper motor.

Augmented Reality for Immersive and Tactile Flight Simulation

Flight simulators based on wrap-around displays provide an immersive experience for pilot training, but not only are they expensive and bulky, they typically only simulate a single aircraft configuration. Virtual reality (VR) is an interesting alternative, providing an immersive experience with lower cost and space requirements, as well as the ability to simulate arbitrary aircraft. Unfortunately, current VR systems do not provide natural tactile interaction with instruments and avionics in the cockpit environment. This article explores augmented reality (AR) as an alternative to VR and wrap-around display flight simulators, delivering an immersive experience for pilot training and tactile interaction with aircraft controls. The concept is demonstrated through a basic general aviation simulator setup, showing that a relatively low-cost personal computer and graphics processing unit can provide AR simulations at useful frame rates.

Automated Complete Blood Cell Count and Malaria Pathogen Detection Using Convolution Neural Network

Complete blood cell count, which indicates the density of different blood cells in the human body is extremely important for evaluating the overall health of a person and also for detecting a wide range of disorders, including anemia, infection and leukemia. Hence, automating this task will not only increase the speed of diagnosis, but also lower the overall treatment cost. In this paper, we focus on using a convolution neural network to perform this complete blood cell count on blood smear images. The network is also trained to detect malarial pathogens in the blood, if present. Experiments show that the overall performance of the system has a mean average precision of over ${\bf 0.95}$ when compared with the ground-truth. Furthermore, the system predicts the images containing malarial parasites as infected ${\bf 100\%}$ of the time. The software is also ported to a low cost microcomputer for rapid prototyping.

From facilitating interactivity to managing hyperconnectivity: 50 years of human–computer studies

Abstract Five decades ago advances in integrated circuits and time-sharing operating systems made interactive use of computers economically feasible. Visions of man-computer symbiosis and the augmentation of man’s intellect became realistic research objectives. The initial focus was on facilitating interactivity through improved interface technology, and supporting its application through good practices based on experience and psychological principles. Within a decade technology advances made low cost personal computers commonly available in the home, office and industry, and these were rapidly enhanced with software that made them attractive in a wide range of applications from games to office automation and industrial process control. Within three decades the Internet enabled human–computer interaction to extend across local, national and international networks, and, within four, smartphones and tablets had made access to computers and networks almost ubiquitous to any person, at any time and any place. Banking, commerce, institutional and company operations, utility and government infrastructures, took advantage of, and became dependent on, interactive computer and networking capabilities to such an extent that they have now been assimilated in our society and are taken for granted. This hyperconnectivity has been a major economic driver in the current millennium, but it has also raised new problems as malevolent users anywhere in the world have become able to access and interfere with critical personal, commercial and national resources. A major issue for human–computer studies now is to maintain and enhance functionality, usability and likeability for legitimate users whilst protecting them from malevolent users. Understanding the issues involved requires a far broader consideration of socio-economic issues than was required five decades ago. This article reviews various models of the role of technology in human civilization that can provide insights into our current problematique.

Development of a low-cost microcomputer based vein imaging system

In this study, a low-cost microcomputer based dorsal hand-vein imaging system was developed by utilizing non-invasive near infrared imaging technology for the patients having hand vein visibility problems. On the microcomputer, we established a new database with high-quality dorsal-hand vein images. Pre- and post-processing were accomplished in Python language using Open-CV library on the microcomputer. Then, the specialist examined the whole obtained raw vein images to detect blood veins by marking the vein areas on a touch screen monitor, namely the reference images. The accuracy of the vein segmentation of our imaging system was assessed by comparing the reference images and the processed images on the microcomputer. We offered a novel simple pixel to pixel algorithm to test the segmented vein areas by the system. In addition, 2-D cross correlation algorithm was used for a secondary analysis. The average accuracy result of the 2-D cross correlation algorithms was 83.84% while pixel-based algorithm offered 94.47%. According to the results, we may conclude that the proposed vein imaging system do not produce suspicious unrelated vein areas which might create another clinical problem for the patients.

A Middle Game Search Algorithm Applicable to Low-Cost Personal Computer for Go

Go Artificial Intellects(AIs) using deep reinforcement learning and neural networks have achieved superhuman performance, but they rely on powerful computing resources. They are not applicable to low-cost personal computer(PC). In our life, most entertainment programs of Go run on the general PC. A human Go master consider different strategies for different stages, especially for the middle stage that has a significant impact on winning or losing. To study arguably a more humanlike approach that is applicable to low-cost PC while not reducing chess power, this paper proposes a new search algorithm based on hypothesis testing and dynamic randomization for the middle stage of the game Go. Firstly, a new method to decide the intervals of different playing stages more reasonable based on hypothesis testing is proposed. Secondly, a new search algorithm including a layered pruning branch method, a comprehensive evaluation function and a new selecting node method is proposed. The pruning method based on domain knowledge and upper confidence bound formula(UCB) are all applied to subtract the branches from the lower evaluation score, which was ranked behind 20%. The comprehensive evaluation function with adjustable parameters is proposed to evaluate the tree nodes after pruning. The new selecting node method based on dynamic randomization is used to expand the tree by selecting a node randomly from the high-quality node interval. Finally, the experimental results show that the designed algorithm outperforms Gnugo3.6 and Gnugo3.8 in chess power while reducing average search time and average RAM cost for one move effectively on a 19×19 board.

Airborne particulate matter monitoring in Kenya using calibrated low-cost sensors

Abstract. East African countries face an increasing threat from poor air quality stemming from rapid urbanization, population growth, and a steep rise in fuel use and motorization rates. With few air quality monitoring systems available, this study provides much needed high temporal resolution data to investigate the concentrations of particulate matter (PM) air pollution in Kenya. Calibrated low-cost optical particle counters (OPCs) were deployed in Kenya in three locations: two in the capital Nairobi and one in a rural location in the outskirts of Nanyuki, which is upwind of Nairobi. The two Nairobi sites consist of an urban background site and a roadside site. The instruments were composed of an AlphaSense OPC-N2 ran with a Raspberry Pi low-cost microcomputer, packaged in a weather-proof box. Measurements were conducted over a 2-month period (February–March 2017) with an intensive study period when all measurements were active at all sites lasting 2 weeks. When collocated, the three OPC-N2 instruments demonstrated good inter-instrument precision with a coefficient of variance of 8.8±2.0 % in the fine particle fraction (PM2.5). The low-cost sensors had an absolute PM mass concentration calibration using a collocated gravimetric measurement at the urban background site in Nairobi.The mean daily PM1 mass concentration measured at the urban roadside, urban background and rural background sites were 23.9, 16.1 and 8.8 µg m−3, respectively. The mean daily PM2.5 mass concentration measured at the urban roadside, urban background and rural background sites were 36.6, 24.8 and 13.0 µg m−3, respectively. The mean daily PM10 mass concentration measured at the urban roadside, urban background and rural background sites were 93.7, 53.0 and 19.5 µg m−3, respectively. The urban measurements in Nairobi showed that PM concentrations regularly exceed WHO guidelines in both the PM10 and PM2.5 size ranges. Following a Lenschow-type approach we can estimate the urban and roadside increments that are applicable to Nairobi (Lenschow et al., 2001). The median urban increment is 33.1 µg m−3 and the median roadside increment is 43.3 µg m−3 for PM2.5. For PM1, the median urban increment is 4.7 µg m−3 and the median roadside increment is 12.6 µg m−3. These increments highlight the importance of both the urban and roadside increments to urban air pollution in Nairobi.A clear diurnal behaviour in PM mass concentration was observed at both urban sites, which peaks during the morning and evening Nairobi rush hours; this was consistent with the high roadside increment indicating that vehicular traffic is a dominant source of PM in the city, accounting for approximately 48.1 %, 47.5 % and 57.2 % of the total PM loading in the PM10, PM2.5 and PM1 size ranges, respectively. Collocated meteorological measurements at the urban sites were collected, allowing for an understanding of the location of major sources of particulate matter at the two sites. The potential problems of using low-cost sensors for PM measurement without gravimetric calibration available at all sites are discussed.This study shows that calibrated low-cost sensors can be successfully used to measure air pollution in cities like Nairobi. It demonstrates that low-cost sensors could be used to create an affordable and reliable network to monitor air quality in cities.

Raspberry Pi-powered imaging for plant phenotyping.

Premise of the StudyImage‐based phenomics is a powerful approach to capture and quantify plant diversity. However, commercial platforms that make consistent image acquisition easy are often cost‐prohibitive. To make high‐throughput phenotyping methods more accessible, low‐cost microcomputers and cameras can be used to acquire plant image data.Methods and ResultsWe used low‐cost Raspberry Pi computers and cameras to manage and capture plant image data. Detailed here are three different applications of Raspberry Pi–controlled imaging platforms for seed and shoot imaging. Images obtained from each platform were suitable for extracting quantifiable plant traits (e.g., shape, area, height, color) en masse using open‐source image processing software such as PlantCV.ConclusionsThis protocol describes three low‐cost platforms for image acquisition that are useful for quantifying plant diversity. When coupled with open‐source image processing tools, these imaging platforms provide viable low‐cost solutions for incorporating high‐throughput phenomics into a wide range of research programs.

Real-Time People Counting System for Customer Movement Analysis

We propose a real-time people-counting system that can be applied in a retail store to estimate the number of people entering and exiting. The proposed method consists of three main procedures: foreground extraction based on the average picture level (APL), a dilated motion search based on the maximum a posteriori probability (MAP), and flow analysis based on multiple touching sections (MTS). We first produce a background model to extract the foreground by using line-wise APL. A dilated motion search with the MAP-based approach is then used to estimate the motions on the line of interest. Next, the flow generated by the foreground on the MTS is analyzed. Finally, the results of the motion estimation and flow analysis are incorporated to produce the number of people entering and exiting the store. We used a low-cost microcomputer to implement the system, which is capable of wireless transmission and is easy to install in a retail environment. Experimental results show that the proposed method provided the best F1 score and accuracy values for the people count results with much lower computational complexity than benchmark methods. In addition, it successfully estimated the number of people entering and exiting the store in real time.

A Microprocessor-based VLF Receiver for Exploration

ABSTRACT A modern low cost microcomputer and accessories were used to create a very low frequency (VLF) receiver for geophysical prospecting. The signals were detected by induction coils, which were then amplified, digitized by a sound card, and analyzed by a Raspberry Pi computer. The computer used a Linux operating system and the Python programming language. The peripheral devices were “off-the-shelf”' commercial items except for the preamplifiers and the induction coils, which were designed and constructed by the authors. A test profile showed typical VLF anomalies across a weak conductor.

Development and evaluation of a self-cleaning custom-built auto sampler controlled by a low-cost RaspberryPi microcomputer for online enzymatic activity measurements

A fully automated on-site device (SAMP-FIL) that enables water sampling with simultaneous filtration and effective cleaning procedures of the device's components was developed and field-tested. The SAMP-FIL was custom-built using commercially available components and was controlled by a RaspberryPi single-board computer operating open-source software. SAMP-FIL was designed for sample pre-treatment with minimal sample alteration to meet the requirements of on-site measurement devices that cannot handle coarse suspended solids within the measurement procedure or cycle. A highly effective cleaning procedure provides a fresh and minimally altered sample for the connected measurement device. The construction and programmed software facilitates the use of SAMP-FIL for different connected measurement devices. The SAMP-FIL sample pretreatment was tested for over one year for rapid and on-site enzymatic activity (beta-d-glucuronidase, GLUC) determination (BACTcontrol) in sediment-laden stream water. The formerly used proprietary sampling set-up was assumed to lead to significant damping of the measurement signal due to its susceptibility to clogging, debris accumulation and bio-film accumulation. The implementation of SAMP-FIL considerably increased the error-free running time and measurement accuracy of BACTcontrol devices. This paper describes how low-cost microcomputers, such as the RaspberryPi, can be used by operators to substantially improve established measuring systems via effective sampling devices. Furthermore, the results of this study highlight the importance of adequate sample pretreatment for the quality of on-site measurements.

GPU-accelerated iterative solution of complex-entry systems issued from 3D edge-FEA of electromagnetics in the frequency domain

We present a performance analysis of a parallel implementation for both preconditioned conjugate gradient and preconditioned bi-conjugate gradient solvers running on graphic processing units (GPUs) with CUDA programming model. The solvers were mainly optimized for the solution of sparse systems of algebraic equations at complex entries, arising from the three-dimensional edge-finite element analysis of the electromagnetic phenomena involved in the open-bound earth diffusion of currents under time-harmonic excitation. We used a shifted incomplete Cholesky (IC) factorization as preconditioner. Results show a significant speedup by using either a single-GPU or a multi-GPU device, compared to a serial central processing unit (CPU) implementation, thereby allowing the simulations of large-scale problems in low-cost personal computers. Additional experiments of the optimized solvers show that its use can be extended successfully to other complex systems of equations arising in electrical engineering, such as those obtained in power–system analysis.

An Automated Measurement Method for High Speed Serial Data Validation

A highly automated way for serial data validation is presented in this paper. The method is based on widely available Digital Storage Oscilloscope (DSO) and low cost Personal Computer (PC). Instrument automation with data capturing and processing is demonstrated. The method is able to process large amount of high speed serial data. The validation procedure is shown in this paper; two instrument automation control protocols are described; data processing algorithm in Python language is also discussed.