Soft-processors commonly find use in FPGA-based systems as a way of introducing software-defined, reprogrammable functionality into a fixed hardware design. The independence of a soft-processor from a specific hardware solution also makes this class of devices an attractive educational resource, allowing students to freely analyze, reconfigure and reimplement a complete microprocessor design. This article showcases the design considerations and implementation of a simple, RISC-like soft-processor core, acting as a demonstration platform for a variety of architectural concepts, while maintaining an approachable level of complexity, as well as not being limited by compatibility with specific programmable logic vendors.

Nitrogen dioxide (NO2) is a well-known air pollutant, mostly elevated by car traffic in cities. To date, small, reliable, cost-efficient multipollutant sensors with sufficient power and accuracy for community-based atmospheric studies are still lacking. The HAPADS (highly accurate and autonomous programmable platforms for providing air pollution data services) platforms, developed and tested in real conditions, can be a possible approach to solving this issue. The developed HAPADS platforms are equipped with three different NO2 sensors (7E4-NO2-5, SGX-7NO2, MICS-2711 MOS) and a combined ambient air temperature, humidity, and pressure sensor (BME280). The platforms were tested during the driving test, which was conducted across various roads, including highways, expressways, and national and regional routes, as well as major cities and the countryside, to analyse the environmental conditions as much as possible (Poland, 2024). The correlation coefficient r was more than 0.8, and RMSE (root mean squared error) was in the 3.3-4.3 mu g/m3 range during the calibration process. The results obtained during the driving tests showed R2 of 0.9-1.0, which proves the ability of HAPADS platforms to work in the hard environmental conditions (including high rain and snow, as well as sun and a wide range of temperatures and humidity).

Air quality significantly impacts the environment and human living conditions, with direct and indirect effects on the economy. Precise and prompt detection of air pollutants is crucial for mitigating risks and implementing strategies to control pollution within acceptable thresholds. One of the common pollutants is nitrogen dioxide (NO2), high concentrations of which are detrimental to the human respiratory system and may lead to serious lung diseases. Unfortunately, reliable NO2 detection requires sophisticated and expensive apparatus. Although cheap sensors are now widespread, they lack accuracy and stability and are highly sensitive to environmental conditions. The purpose of this study is to propose a novel approach to precise calibration of the low-cost NO2 sensors. It is illustrated using a custom-developed autonomous platform for cost-efficient NO2 monitoring. The platform utilizes various sensors alongside electronic circuitry, control and communication units, and drivers. The calibration strategy leverages comprehensive data from multiple reference stations, employing neural network (NN) and kriging interpolation metamodels. These models are built using diverse environmental parameters (temperature, pressure, humidity) and cross-referenced data gathered by surplus NO2 sensors. Instead of providing direct outputs of the calibrated sensor, our approach relies on predicting affine correction coefficients, which increase the flexibility of the correction process. Additionally, a calibration stage incorporating global correlation enhancement is developed and applied. Demonstrative experiments extensively validate this approach, affirming the platform and calibration methodology’s practicality for reliable and cost-effective NO2 monitoring, especially keeping in mind that the predictive power of the enhanced sensor (correlation coefficient nearing 0.9 against reference data, RMSE < 3.5 µg/m3) is close to that of expensive reference equipment.

This paper presents an optimized approach to designing OTA-C filters with a focus on reducing the number of components while maintaining or improving performance. By employing shared sub-blocks within the transconductance amplifier architecture, the proposed method minimizes power consumption and circuit area, ensuring efficient integration in modern electronic systems. The paper also discusses the theoretical basis for the design, implementation details, and performance evaluation of the optimized filters. The results demonstrate significant improvements in resource efficiency and scalability compared to conventional designs.

Particulate matter (PM) stands out as a highly perilous form of atmospheric pollution, posing significant risks to human health by triggering or worsening numerous heart, brain, and lung ailments, and even increasing the likelihood of cancer and premature mortality. Therefore, ensuring accurate monitoring of PM levels holds paramount significance, particularly urban zones of dense population. Still, achieving precise readings of PM concentration demands the use of bulky and costly equipment, typically stationed at widely spaced reference sites. The rise in popularity of low-cost PM sensors as potential substitutes has been noted, although their reliability is hampered by manufacturing flaws, instability, and susceptibility to environmental variations. In this work, we introduce a novel approach to field calibration for cheap PM sensors. Our method integrates multiplicative and additive corrections, with coefficients determined by an artificial neural network (ANN) surrogate. The ANN model accounts for environmental parameters and the sensor's PM readings as inputs, with its architecture fine-tuned to ensure optimal generalization capability. Additionally, we consider an extended set of input parameters, including local temporal changes of environmental variables, and short sequences of low-sensor readings, to further enhance calibration reliability. We validate our technique using a non-stationary measurement equipment alongside reference data acquired by government-approved reference stations in Gdansk, Poland. The obtained values of coefficients of determination reach as high as 0.89 for PM1, 0.87 for PM2.5, and 0.77 for PM10, respectively, while the root mean square error (RMSE) is merely 3.0, 3.9, and 4.9 µg/m³. Such a performance positions the calibrated low-cost sensor as a potential alternative to stationary measurement equipment.

Accurate tracking of harmful gas concentrations is essential to swiftly and effectively execute measures that mitigate the risks linked to air pollution, specifically in reducing its impact on living conditions, the environment, and the economy. One such prevalent pollutant in urban settings is nitrogen dioxide (NO2), generated from the combustion of fossil fuels in car engines, commercial manufacturing, and food processing. Its elevated levels have adverse effects on the human respiratory system, exacerbating asthma and potentially causing various lung diseases. However, precise monitoring of NO2 requires intricate and costly equipment, prompting the need for more affordable yet dependable alternatives. This paper introduces a new method for reliably calibrating cost-effective NO2 sensors by integrating machine learning with neural network surrogates, global data scaling, and an expanded set of correction model inputs. These inputs encompass differentials of environmental parameters (such as temperature, humidity, atmospheric pressure), as well as readings from both primary and supplementary low-cost NO2 detectors. The methodology was showcased using a purpose-built platform housing NO2 and environmental sensors, electronic control units, drivers, and a wireless communication module for data transmission. Comparative experiments utilized NO2 data acquired during a five-month measurement campaign in Gdansk, Poland, from three independent high-precision reference stations, and low-cost sensor data gathered by the portable measurement platforms at the same locations. The numerical experiments have been carried out using several calibration scenarios using various sets of calibration input, as well as enabling/disabling the use of differentials, global data scaling, and NO2 readings from the primary sensor. The results validate the remarkable correction quality, exhibiting a correlation coefficient exceeding 0.9 concerning reference data, with a root mean squared error below 3.2 mu g/m3. This level of performance positions the calibrated sensor as a dependable and cost-effective alternative to expensive stationary equipment for NO2 monitoring.

Air pollution remains a considerable contemporary challenge affecting life quality, the environment, and economic well-being. It encompasses an array of pollutants-gases, particulate matter, biological molecules-emanating from sources such as vehicle emissions, industrial activities, agriculture, and natural occurrences. Nitrogen dioxide (NO2), a harmful gas, is particularly abundant in densely populated urban areas. Given its detrimental impact on health and the environment, precise monitoring of NO2 levels is crucial for devising effective strategies to mitigate risks. However, precise measurement of NO2 presents challenges as it traditionally relies on expensive and heavy (therefore, stationary) equipment. This has led to the pursuit of more affordable alternatives, though their dependability is frequently questionable. This study introduces an innovative technique for precise calibration of low-cost NO2 sensors. Our methodology involves statistical preprocessing of sensor measurements to align their distributions with reference data. The core of the calibration model is an artificial neural network (ANN), trained to synchronize sensor and reference time series measurements. It incorporates environmental variables such as temperature, humidity, and atmospheric pressure, along with readings from redundant NO2 sensors for cross-referencing, and short time series of primary sensor NO2 measurements. This enables efficient learning of typical sensor changes over time in relation to these factors. Additionally, an interpolative kriging model serves as an auxiliary surrogate to enhance the correction process's reliability. Validation using an autonomous monitoring platform from Gdansk University of Technology, Poland, and public reference station data gathered over five months shows remarkable calibration accuracy, with a correlation coefficient close to 0.95 and RMSE of 2.4 mu g/m3. These results position the corrected sensor as an attractive and cost-effective alternative to conventional NO2 measurement methods.

Due to detrimental effects of atmospheric particulate matter (PM), its accurate monitoring is of paramount importance, especially in densely populated urban areas. However, precise measurement of PM levels requires expensive and sophisticated equipment. Although low-cost alternatives are gaining popularity, their reliability is questionable, attributed to sensitivity to environmental conditions, inherent instability, and manufacturing imperfections. The objectives of this paper include (i) introduction of an innovative approach to field calibration for low-cost PM sensors using artificial intelligence methods, (ii) implementation of the calibration procedure involving optimized artificial neural network (ANN) and combined multiplicative and additive correction of the low-cost sensor readings, (iii) demonstrating the efficacy of the presented technique using a custom-designed portable PM monitoring platform and reference data acquired from public measurement stations. The results obtained through comprehensive experiments conducted using the aforementioned low-cost sensor and reference data demonstrate remarkable accuracy for the calibrated sensor, with correlation coefficients of 0.86 for PM1 1 and PM2.5, 2.5 , and 0.76 PM10 10 (particles categorized as having diameter equal to or less than 1 mu m, 2.5 mu m, and 10 mu m, respectively), along with low RMSE values of only 3.1, 4.1, and 4.9 mu g/m 3 .

Air pollution stands as a significant modern-day challenge impacting life quality, the environment, and the economy. It comprises various pollutants like gases, particulate matter, biological molecules, and more, stemming from sources such as vehicle emissions, industrial operations, agriculture, and natural events. Nitrogen dioxide (NO2), among these harmful gases, is notably prevalent in densely populated urban regions. Given its adverse effects on health and the environment, accurate monitoring of NO2 levels becomes imperative for devising effective risk mitigation strategies. However, the precise measurement of NO2 poses challenges as it traditionally relies on costly and bulky equipment. This has prompted the development of more affordable alternatives, although their reliability is often questionable. The aim of this article is to introduce a groundbreaking method for precisely calibrating cost-effective NO2 sensors. This technique involves statistical preprocessing of low-cost sensor readings, aligning their distribution with reference data. Central to this calibration is an artificial neural network (ANN) surrogate designed to predict sensor correction coefficients. It utilizes environmental variables (temperature, humidity, atmospheric pressure), cross-references auxiliary NO2 sensors, and incorporates short time series of previous readings from the primary sensor. These methods are complemented by global data scaling. Demonstrated using a custom-designed cost-effective monitoring platform and high-precision public reference station data collected over 5 months, every component of our calibration framework proves crucial, contributing to its exceptional accuracy (with a correlation coefficient near 0.95 concerning the reference data and an RMSE below 2.4 µg/m3). This level of performance positions the calibrated sensor as a viable, cost-effective alternative to traditional monitoring approaches.

Air quality significantly impacts human health, the environment, and the economy. Precise real-time monitoring of air pollution is crucial for managing associated risks and developing appropriate short- and long-term measures. Nitrogen dioxide (NO2) stands as a common pollutant, with elevated levels posing risks to the human respiratory tract, exacerbating respiratory infections and asthma, and potentially leading to chronic lung diseases. Notwithstanding, precise NO2 detection typically demands complex and costly equipment. This paper explores NO2 monitoring using low-cost platforms, meticulously calibrated for reliability. An integrated measurement unit is first presented that contains primary and supplementary nitrogen dioxide sensors, as well as auxiliary detectors for evaluating outside and inside temperature and humidity. The calibration process utilizes data acquired over the period of five months from various reference stations. Employing machine learning with an artificial neural network (ANN)-based and kriging interpolation surrogate models, the correction strategy integrates additive and multiplicative enhancement, predicted by the ANN through auxiliary sensor data such as temperature, humidity, and the sensor-detected NO2 levels. Extensive verification studies showcase that this calibration approach notably enhances monitoring precision (coefficient of determination surpassing 0.85 concerning reference data, and RMSE of less than four μg/m3), rendering low-cost NO2 detection practical and dependable.

Atmospheric particulate matter (PM) poses a significant threat to human health, infiltrating the lungs and brain and leading to severe issues such as heart and lung diseases, cancer, and premature death. The main sources of PM pollution are vehicular and industrial emissions, construction and agricultural activities, and natural phenomena such as wildfires. Research underscores the absence of a safe threshold for particulate exposure, highlighting the crucial need for monitoring PM levels to develop and implement effective risk mitigation measures. Notwithstanding, accurate measurement of PM concentration relies on expensive and cumbersome equipment. Despite the rising popularity of low-cost alternatives, their reliability remains questionable, given their sensitivity to environmental conditions, inherent instability, and manufacturing imperfections. This article proposes a novel approach to efficient correction of low-cost PM sensors. The primary calibration model is a feedforward artificial neural network (ANN), which directly renders predicted output of the corrected sensor based on environmental variables such as temperature, humidity, and atmospheric pressure. The ANN hyper-parameters are identified by aligning time series of prior reference and low-cost sensor readings, which enables the network to learn typical temporal changes of the sensor outcome as a function of the aforementioned parameters as well as operational relationships between the sensor and the reference device. The architecture of the ANN is optimized in terms of the number of neurons in each layer to enhance its generalization capability. Our methodology has been demonstrated using a custom-designed portable monitoring platform and reference data acquired from public stations in Gdansk. The results are indicative of excellent calibration reliability. The achieved correlation coefficients w.r.t. the reference readings are 0.86, 0.88, and 0.72 for PM1, PM10, and PM2.5, respectively, whereas RMSE values are only 3.0, 3.9, and 5.4 µg/m³.

Nitrogen dioxide (NO2) is a prevalent air pollutant, particularly abundant in densely populated urban regions. Given its harmful impact on health and the environment, precise real-time monitoring of NO2 concentration is crucial, particularly for devising and executing risk mitigation strategies. However, achieving precise measurements of NO2 is challenging due to the need for expensive and cumbersome equipment. This has spurred the development of more affordable alternatives, although their reliability is often uncertain. The aim of this study is to present a new method for accurately calibrating low-cost NO2 sensors. Our approach utilizes artificial intelligence techniques, particularly neural networks (NNs) acting as surrogates, trained to forecast sensor correction coefficients. These predictions rely on environmental variables (temperature, humidity, etc.), data from additional nitrogen dioxide sensors, and a short series of previous NO2 readings from the main sensor, all serving as inputs for the NN metamodel. As shown, integrating short -time -scale previous measurements significantly improves the quality of the calibration process, further bolstered by global response correction. Similar enhancements are achieved by considering environmental parameter differentials. Our calibration approach has been validated using a custom-built, cost-efficient monitoring platform and reference data collected over five months from high-performance public stations in Gdansk, Poland. The results demonstrate outstanding correction quality, with a correlation coefficient close to 0.93 compared to the reference data and an RMSE below 2.8 mu g/ m3. This establishes the calibrated sensor as a practical and cost-effective alternative to expensive traditional NO2 monitoring stations.

Development of present-day antenna systems is an intricate and multi-step process requiring, among others, meticulous tuning of designable (mainly geometry) parameters. Concerning the latter, the most reliable approach is rigorous numerical optimization, which tends to be resource-intensive in terms of computing due to involving full-wave electromagnetic (EM) simulations. The cost-related issues are particularly pronounced whenever global optimization is necessary, typically carried out using nature-inspired algorithms. Although capable of escaping from local optima, population-based algorithms exhibit poor computational efficiency, to the extent of being hardly feasible when directly handling EM simulation models. A popular mitigation approach involves surrogate modeling techniques, facilitating the search process by replacing costly EM analyses with a fast metamodel. Yet, surrogate-assisted procedures feature complex implementations, and their range of applicability is limited in terms of design space dimensionality that can be efficiently handled. Rendering reliable surrogates is additionally encumbered by highly nonlinear antenna characteristics. This paper investigates potential benefits of employing problem-relevant knowledge in the form of response features into nature-inspired antenna optimization. As demonstrated in the recent literature, re-formulating the design task with the use of appropriately selected characteristic locations of the antenna responses permits flattening the functional landscape of the objective function, leading to faster convergence of optimization procedures. Here, we apply this concept to nature-inspired global optimization of multi-band antenna structures, and demonstrate its relevance, both in terms of accelerating the search process but also improving its reliability. The advantages of feature-based nature-inspired optimization are corroborated through comprehensive (based on three antenna structures) comparisons with a population-based search involving conventional (e.g., minimax) design problem formulation.

Air pollution involves multiple health and economic challenges. Its accurate and low-cost monitoring is important for developing services dedicated to reduce the exposure of living beings to the pollution. Particulate matter (PM) measurement sensors belong to the key components that support operation of these systems. In this work, a modular, mobile Internet of Things sensor for PM measurements has been proposed. Due to a limited accuracy of the PM detector, the measurement data are refined using a two-stage procedure that involves elimination of the non-physical signal spikes followed by a non-linear correction of the responses using a multiplicative surrogate model. The correction layer is derived from the sparse and non-uniform calibration data, i.e., a combination of the measurements from the PM monitoring station and the sensor obtained in the same location over a specified (relatively short) interval. The device and the method have been both demonstrated based on the data obtained during three measurement campaigns. The proposed correction scheme improves the fidelity of PM measurements by around two orders of magnitude w.r.t. the responses for which the post-processing has not been considered. Performance of the proposed surrogate-assisted technique has been favorably compared against the benchmark approaches from the literature.

Contribution: While research indicates usefulness of remote laboratories in teaching of digital hardware systems, their main application is to complement stationary classes. This work presents a low-cost, scalable architecture that supports rapid transformation of teaching to a model based solely on remote access mechanisms. Background: Adaptation of online laboratory solutions from the literature to en-masse teaching of digital circuits is time-consuming and expensive. Solutions that permit cheap and rapid conversion of courses to remote environments seem to be of high value, especially when social distancing renders direct teaching impossible. Intended Outcomes: Demonstration of a flexible and cheap architecture that permits rapid transformation of digital circuits laboratories to the remote environment. Validation of system's performance and usefulness on a large group of students. Application Design: Remote digital circuits laboratories from the literature are designed to complement stationary classes. For successful teaching during pandemic, a low-cost, flexible, and efficient solution to online laboratory based on commonly available technologies is required. Findings: The proposed remote laboratory architecture enables rapid conversion of on-site teaching to online model, while supporting in-situupgrades and functionality enhancements. The presented solution proved to be a convenient substitute for conventional laboratories during pandemic.

In this article, a stress monitoring system tailored for individuals with Autism Spectrum Disorders (ASD) and developed for the educational institution is presented. People with ASD face problems with effective stress management due to their high self-perceived levels of stress, poor ability to cope with it, and difficulties with the accurate detection of the source of stress. Consistently, being able to measure stress appears to be highly vital for this specific group. The design and construction of the measuring system are introduced. The stress monitoring system including autonomic wearable device (wristband), both the electronics and mechanical part, as well as the software application for data analysis are shown. The wearable device includes sensors for measuring heart rate, skin resistance, temperature and movement. A dedicated software application allows for generating reports to evaluate therapeutic effects. The complex stress monitoring system has been tested in an educational institution. It can provide significant support in the stress management of people with ASD and become a meaningful assistance in the therapy of neurodiverse individuals.

This paper presents an algorithm for the measurement of the human heart rate, using photoplethysmography (PPG), i.e., the detection of the light at the skin surface. The signal from the PPG sensor is processed in time-domain; the peaks in the preprocessed and conditioned PPG waveform are detected by using a peak detection algorithm to find the heart rate in real time. Apart from the PPG sensor, the accelerometer is also used to detect body movement and to indicate the moments in time, for which the PPG waveform can be unreliable. This paper describes in detail the signal conditioning path and the modified algorithm, and it also gives an example of implementation in a resource-constrained wrist-wearable device. The algorithm was evaluated by using the publicly available PPG-DaLia dataset containing samples collected during real-life activities with a PPG sensor and accelerometer and with an ECG signal as ground truth. The quality of the results is comparable to the other algorithms from the literature, while the required hardware resources are lower, which can be significant for wearable applications.

The application of a Bluetooth skin resistance sensor in assisting people with Autism Spectrum Disorders (ASD), in their day-to-day work, is presented in this paper. The design and construction of the device are discussed. The authors have considered the best placement of the sensor, on the body, to gain the most accurate readings of user stress levels, under various conditions. Trial tests were performed on a group of sixteen people to verify the correct functioning of the device. Resistance levels were compared to those from the reference system. The placement of the sensor has also been determined, based on wearer convenience. With the Bluetooth Low Energy block, users can be notified immediately about their abnormal stress levels via a smartphone application. This can help people with ASD, and those who work with them, to facilitate stress control and make necessary adjustments to their work environment.

In this paper a programmable input mode instrumentation amplifier (IA) utilising second generation, multiple output current conveyors and transmission gates is presented. Its main advantage is the ability to choose a voltage or current mode of inputs by setting the voltage of two configuration nodes. The presented IA is prepared as an integrated circuit block to be used alone or as a sub-block in a microcontroller or in a field programmable gate array (FPGA), which shall condition analogue signals to be next converted by an analogue-to-digital converter (ADC). IA is designed in AMS 0.35 µm CMOS technology and the power supply is 3.3 V; the power consumption is approximately 9.1 mW. A linear input range in the voltage mode reaches ± 1.68 V or ± 250 µA in current mode. A passband of the IA is above 11 MHz. The amplifier works in class A, so its current supply is almost constant and does not cause noise disturbing nearby working precision analogue circuits.