Low-cost Devices Research Articles

Device-free crowd counting using multi-link WiFi CSI for occupant-driven energy management of HVAC systems

ABSTRACT Accurate estimation of the number of occupants in a building’s thermal zone is a crucial input for the dynamic estimation of the cooling load of a heating, ventilation, and air conditioning equipment. This data, in conjunction with an intelligent control algorithm, can be judiciously employed for enhancing occupant comfort and achieving substantial energy savings through demand-driven operation. In this work, we propose a WiFi channel state information (CSI) based passive people counting model that utilizes a wavelet-based signal denoising scheme and an artificial neural network (ANN) model for automatic feature extraction. The application of the proposed model on a public dataset shows more than 99% accuracy in detecting up to eight people in three rooms of varying sizes. The proposed model was also validated on CSI samples collected from a laboratory environment with five occupants, using ESP32, a low cost and low power IoT device. The results show that the proposed model can perform people counting with an accuracy of up to 98%, using single- and multi-link measurements.

Real-time Performance Comparison of Face Detection Algorithms using Raspberry Pi

Abstract This study reviewed state-of-the-art face-detection techniques like Haar cascade, Dlib HOG, MTCNN, and MediaPipe; implemented and tested them on Raspberry Pi, and evaluated their accuracy, speed, and frames per second. Overall, the research underscores the practical challenges of face detection, including varying lighting, facial expressions, occlusions, poses, scale of face, and accessories, and provides valuable insights for developers and researchers working on edge AI applications on low-cost edge devices. The study found that the MediaPipe face detection algorithm demonstrated robust performance, even with low-quality images, and showed good efficiency and resource management on the Raspberry Pi. The research emphasized the importance of considering factors like accuracy, speed, and resource efficiency in face detection on edge devices. The findings suggest that MediaPipe is a strong candidate for applications requiring efficient face detection, affordable and versatile platforms like Raspberry Pi. By focusing on the Raspberry Pi, the study offers a unique perspective on the performance of state-of-the-art face detection algorithms in real-world, resource-constrained environments, making it a significant contribution to the field of face recognition and edge computing.

A spurious-free SAW filter employing a novel transducer structure on bulk LiNbO3 substrates

In order to cope with the ubiquitous wireless connections and the rapid transmission of data in communication systems, surface acoustic wave filters are required to have high performance. With a large electromechanical coupling coefficient (K2), conventional bulk 64°YX-LiNbO3 substrates are attractive for low-cost mass production of surface acoustic wave (SAW) resonators. However, the influence of the transverse modes restricts their practical application. In this work, a novel and simple tilted transducer is fabricated, and successfully achieved spurious-free passband SAW filters based on 64°YX-LN piezoelectric substrates. In this work, the transverse modes suppression method is theoretically studied and designed, and the effectiveness of the energy concentrated structure is verified. The optimal tilt IDT angle for eliminating the influence of transverse modes in Al/64 °YX-LN structure is about 6°. Subsequently, based on the above research, three types of tilted resonators were designed and fabricated, which not only verified the reliability of the aforementioned results but also enhanced the performance of the devices, compensating for the deficiency caused by the reduction of the Q factor due to the tilted structure. Finally, based on the double busbar tilted structure, a SAW filter was designed, which has a large 3 dB fractional bandwidth (FBW) and a spurious-free passband. The SAW filter designed in this work has low cost and excellent performance, and can be mass produced. In the field of low-cost RF devices, this work contributes to maintaining the competitive advantage of SAW technology.

Low-Cost Device for Measuring Wastewater Flow Rate in Open Channels

This research paper describes the development of a low-cost device for measuring wastewater flow rates in open channels, a significant advancement enabled by the evolution of microcomputers and processing techniques. A laboratory setup was constructed to validate the device’s accuracy against a standard flow measurement method, optimizing key parameters to achieve a linear relationship between detected and set flow rates, while considering hardware limitations and energy efficiency. The central focus of the research was developing a method to measure the velocity of contaminated fluid using ultrasonic signals, employing the cross-correlation method for signal delay analysis in a stochastic environment. This was complemented by a procedure to measure fluid levels, also based on ultrasonic signals. The device’s reliability was assessed through repeatability and uncertainty measurements, confirming its accuracy with an extended uncertainty not exceeding an average of 3.47% for flows above 40 L/min. The device has potential to provide valuable data on the operational dynamics of sanitary networks, crucial for developing and calibrating simulation models.

Laser-induced graphene-based aptasensor for the selective detection of Escherichia coli in urine samples

A Laser-Induced Graphene-based (LIG) electrode covalently functionalized with an aptamer (P12-55) was used to develop an aptasensor detecting Escherichia coli in urine samples. Recent strides in material science have spotlighted LIG for exceptional attributes like robust mechanical resistance, superior conductivity, extensive surface area, and facile synthesis/patterning on various polymeric substrates. Variations in the aptasensor charge transfer resistance upon interaction with bacterial cells were evaluated by electrochemical impedance spectroscopy. Tests in phosphate buffer saline solution showed the aptasensor linear response for E. coli between 100 and 103 CFU/mL. The sensor proved to be selective for E. coli as a negligible response was observed in the presence of Staphylococcus aureus or Pseudomonas aeruginosa. Finally, the sensor was calibrated in urine samples spiked with a known concentration of E. coli cells. Its characteristics make the aptasensor viable for low-cost and portable devices identifying pathogenic microorganisms at the point-of-need. Due to the short response time (about 30 min), we believe that these sensing devices may significantly improve control and management of urinary tract infections.

Evaluation of phenolics in the analysis of virgin olive oil using near infrared spectroscopy

Olive oil is an indispensable part of the diet in Mediterranean regions, and is appreciated worldwide for its sensory characteristics, combining a fine aroma and pleasant flavor with the high nutritional value of specific chemical compounds. However, making rapid measurements of phenolic compounds is a major challenge for the olive oil sector. The development of a new method based on near infrared (NIR) spectroscopy may be considered an important advance for the sector, as it is rapid, low-cost, non-contaminant and non-destructive. In this study, three different NIR instruments – one FT-NIR benchtop instrument (671.82–2702.70 nm) and two low-cost portable devices (900–1700 nm and 1350–2150 nm) – were used to analyze a collection of virgin olive oil samples from various Mediterranean regions. To predict both the individual and total concentration of phenols in the olive oil, four signal pretreatment methods and modified partial least squares regression analyses were employed to develop the predictive models. The results showed that the benchtop FT-NIR instrument performed better than the other portable instruments when measuring the phenolic compounds in virgin olive oil. The best models of hydroxytyrosol derivatives, tyrosol derivatives, total phenols and “EFSA phenols” showed R2cv values of 0.84, 0.85, 0.88 and 0.89, while the RPDcv values were 2.51, 2.61, 2.93 and 2.95, respectively. Meanwhile, for the portable NIR instruments, the prediction models for hydroxytyrosol derivatives, total phenols and “EFSA phenols” (the sum of the hydroxityrosol and tyrosol derivatives) showed R2cv values ranging between 0.75 and 0.81 and an RPDcv between 2 and 2.5. These results indicate the great potential of NIR, both with benchtop and portable devices, to detect phenolic compounds in virgin olive oil, which can guarantee the quality of virgin olive oil and thus aid progress in the olive oil industry. Moreover, this is the first published work to determine phenolic compounds in virgin olive oil using portable NIR instruments.

Optimizing Plant Disease Classification with Hybrid Convolutional Neural Network–Recurrent Neural Network and Liquid Time-Constant Network

This paper addresses the practical challenge of detecting tomato plant diseases using a hybrid lightweight model that combines a Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). Traditional image classification models demand substantial computational resources, limiting their practicality. This study aimed to develop a model that can be easily implemented on low-cost IoT devices while maintaining high accuracy with real-world images. The methodology leverages a CNN for extracting high-level image features and an RNN for capturing temporal relationships, thereby enhancing model performance. The proposed model incorporates a Closed-form Continuous-time Neural Network, a lightweight variant of liquid time-constant networks, and integrates Neural Circuit Policy to capture long-term dependencies in image patterns, reducing overfitting. Augmentation techniques such as random rotation and brightness adjustments were applied to the training data to improve generalization. The results demonstrate that the hybrid models outperform their single pre-trained CNN counterparts in both accuracy and computational cost, achieving a 97.15% accuracy on the test set with the proposed model, compared to around 94% for state-of-the-art pre-trained models. This study provides evidence of the effectiveness of hybrid CNN-RNN models in improving accuracy without increasing computational cost and highlights the potential of liquid neural networks in such applications.

High Impedance Active Probe for High Voltages

The probe was designed for the measurement of DC voltages of up to 30 kV from high impedance sources. It is based on a resistive divider with a total resistance of 200 GΩ and a step-down factor of 10’000. In order to allow the measurement of the stepped down voltage with a conventional multimeter without loading, the signal was buffered with an operational amplifier. The device was calibrated against a commercial probe using a low impedance high voltage source. A linear relationship was obtained for a high impedance resistive ladder for voltages between 3 and 30 kV, with a coefficient of determination (R2) of 0.9999. The low-cost device (ca. US $200) fills an application niche not addressed by commercial products.

Selection and evaluation of commercial low-cost devices for indoor air quality monitoring in schools

The use of low-cost sensors for monitoring Indoor Air Quality (IAQ) in schools has shown promising results, with the commercialisation of these devices increasing worldwide. This study aims to identify an effective, commercially available low-cost device for IAQ monitoring in schools.Four low-cost devices (AirVisual Pro, PocketLab Air, PurpleAir PA-II-SD and uRAD Monitor A3) were selected for both qualitative and performance evaluations. Field tests were conducted in six indoor microenvironments within a nursery and a primary school. The low-cost devices were co-located with research-grade instruments to obtain reference concentrations of PM1, PM2.5, PM10, CO2, VOC, formaldehyde and O3.The qualitative evaluation revealed limitations, including data loss, negative or erratic values, inconsistent timestamps, and connectivity issues. PM low-cost devices exhibited better performance during non-occupancy periods but faced challenges during periods of occupancy. All devices tended to underestimate measurements compared to reference data, although AirVisual Pro performed better for PM2.5 during occupancy. Furthermore, CO2 concentrations were slightly overestimated, showing improved accuracy during occupancy periods. While formaldehyde peaks were detected by the low-cost device, overall performance was weak for both formaldehyde and O3.The results indicated that AirVisual Pro demonstrated the best overall performance, and presents itself as a promising tool for IAQ monitoring in schools. However, performance evaluations should be tailored to specific microenvironments and occupancy periods. Despite some acceptable performance results, real-context use of the selected low-cost device should be preceded by a proper calibration. Additionally, long-term performance evaluation should be considered in future studies.

Synthesis of chemical bath deposited Manganese oxide thin films for high performance supercapacitor

The present study addresses the growing need for high-performance supercapacitors, emphasizing the benefits of Manganese oxide (MnO2) thin film as a promising electrode material. Thin film deposition of MnO2 is achieved through Chemical Bath Deposition (CBD) method by varying the deposition time on the stainless steel substrate. In order to investigate impact of deposition time on various physico-chemical properties of MnO2 thin films, different characterizations were carried out like X-ray diffraction, Scanning electron microscopy, Energy dispersive X-ray analysis, X-ray photoelectron spectroscopy, cyclic voltammetry, Galvanostatic charge–discharge analysis. The deposited MnO2 thin films are subsequently studied for their electrochemical properties in a 1 M KOH electrolyte solution. Remarkably, the best specific capacitance is obtained about 296.5 F/g at current density of 0.5 A/g, with 86.42 % retention after 5000 cycles, highlighting the excellent electrochemical performance of the MnO2 thin films deposited at 6 hrs of reaction time. This work highlights valuable insights into the CBD deposited MnO2 thin films for supercapacitor applications, providing a foundation for the development of an efficient, low-cost, and scalable energy storage devices.

Evaluation of a low-cost portable NIRS device for monitoring muscle ischemia.

The main objective of this study is to evaluate the low-cost, open-source HEGduino device as a tissue oximetry monitor to advance the research of somatic NIRS monitoring. Specifically, this study analyzes the use of this portable functional NIRS system for detecting the cessation of blood flow due to vascular occlusion in an upper limb.19 healthy patients aged between 25 and 50 were recruited and monitored using HEGduino device. Participants underwent a vascular occlusion test on one forearm. Raw values collected by HEGduino as well as the processed variables derived from the measurements were registered. Additional variables to characterize the signal noise during the tests were also recorded.The results of the data distribution curves for all the subjects in the study accurately detected the physiological events associated with transient tissue ischemia. The statistical analysis of the recorded data showed that the difference between the baseline values recorded by the red led (RED) and its normalized minimum variable was always different from zero (p < 0.014). Furthermore, the difference between the normalized baseline values recorded by the infrared led (IR) and the corresponding normalized minimum value was also different from zero (p < 0.001). The R-squared coefficient of determination for the noise variables considered in this study on the normalized RED and IR values was 0.08 and 0.105, respectively.The study confirms the potential of HEGduino system to detect an interruption of the blood flow by means of variations in regional tissue oxygen saturation. This study demonstrates the potential of the HEGduino device as a monitoring alternative to advance the study of the applicability of NIRS in muscle tissue oximetry.

Development and implementation of a low-cost metal detector device

Development and implementation of a low-cost metal detector device

A predictive model for the estimation of industrial [formula omitted] emissions for IoT-based devices

The paper is devoted to solving the problem, associated with increasing the forecasting accuracy of pollutant concentration level based on PM2.5 dust. The LANN model (LANN – Lagrange powered Artificial Neuron Network), proposed in the paper, allows you to take into account the mutual influence of pollution sources on the concentration level at monitoring points, which, on the one hand, allows to manage the volume of emissions from industrial enterprises in such a way as to avoid penalties and, on the other hand, allows the regulatory agencies to determine points for environmental control, at which permissible standards will be most likely exceeded. The suggested solution makes it possible to use low-cost IoT-based air quality monitoring devices, which were primarily considered not applicable due to their low accuracy and exposure to the influence of short-term stochastic factors; besides, no models were available, which would be capable to provide the necessary accuracy and forecast horizon on the base of IoT data. At the same time, the obtained model has low requirements for computing resources compared to dispersion models and is dependent on the accuracy of weather information; besides, the model allows you to take into account trends and obtain better forecast values in terms of accuracy than the models, that use regression methods, neural network models and ML models, stochastic models and their ensembles. The application of the model improves well-known pollutant dispersion estimation calculation techniques through the use of measurement data flow and dynamic adaptation to changing conditions The model was trained on the dataset over the period 2022-2023 collected from 5 monitoring devices, which were installed in the catchment area of the group, incl. 13 industrial points of PM2.5 pollutant sources.

Harnessing the potential of electrospun TiO2 nanofibers and nanoparticles enriched with natural dyes: a path towards affordable aolutions for low-cost electronic devices

Abstract This study evaluates the morphological effects of TiO2 nanoparticles, nanofibers, and a bilayer configuration on electronic devices, such as Dye-Sensitized Solar Cells (DSSCs) and UV sensors. Cost-efficient natural dyes—curcumin, coffee beans, and banana peel—were used as sensitizers for nanomaterial films. TiO2 nanoparticles were synthesized using the sol–gel technique, while nanofibers were produced via electrospinning. Characterization techniques, including Scanning Electron Microscopy (SEM), Energy Dispersive x-ray Spectroscopy (EDS), and x-ray Diffraction (XRD), confirmed the formation and dimensions of the TiO2 nanostructures. UV–visible spectroscopy was used to determine the optical properties of the samples. TiO2 nanofibers and nanoparticles exhibited high surface-area-to-volume ratios, with nanofibers having a diameter of 20 nm and particles measuring 50 nm. A binder-free, low-temperature paste was prepared using TiO2 nanoparticles and nanofibers to develop thin films. The turmeric dye showed peak absorption at 470 nm with a band gap energy of 2.06 eV when loaded on a TiO2 bilayer film. This study aims to develop electronic devices that reduce costs and enhance performance by using low-cost, efficient, and economically viable dyes. TiO2 nanofiber and nanoparticle films show promise for cost-effective and high-performance electronic devices.

Publisher's Note: "Low-cost device for ratiometric fluorescence measurements" [Rev. Sci. Instrum. 70, 4466-4470 (1999)

Publisher's Note: "Low-cost device for ratiometric fluorescence measurements" [Rev. Sci. Instrum. 70, 4466-4470 (1999)

Development of a low-cost home automation device using TSOP1738 infrared sensor and Arduino microcontroller

Development of a low-cost home automation device using TSOP1738 infrared sensor and Arduino microcontroller

Automated analysis of digital medical images in cervical cancer screening: A systematic review.

Cervical cancer screening programs are poorly implemented in LMICs due to a shortage of specialists and expensive diagnostic infrastructure. To address the barriers of implementation researchers have been developing low-cost portable devices and automating image analysis for decision support.However, as the knowledge base is growing rapidly, progress on the implementation status of novel imaging devices and algorithms in cervical cancer screening has become unclear. The aim of this project was to provide a systematic review summarizing the full range of automated technology systems used in cervical cancer screening. A search on academic databases was conducted and the search results were screened by two independent reviewers. Study selection was based on eligibility in meeting the terms of inclusion and exclusion criteria which were outlined using a Population, Intervention, Comparator and Outcome framework. 17 studies reported algorithms developed with source images from mobile device, viz. Pocket Colposcope, MobileODT EVA Colpo, Smartphone Camera, Smartphone-based Endoscope System, Smartscope, mHRME, and PiHRME. While 56 studies reported algorithms with source images from conventional/commercial acquisition devices. Most interventions were in the feasibility stage of development, undergoing initial clinical validations. Researchers have proven superior prediction performance of computer aided diagnostics (CAD) in colposcopy (>80% accuracies) versus manual analysis (<70.0% accuracies). Furthermore, this review summarized evidence of the algorithms which are being created utilizing portable devices, to circumvent constraints prohibiting wider implementation in LMICs (such as expensive diagnostic infrastructure). However clinical validation of novel devices with CAD is not yet implemented adequately in LMICs.

Peecheck 2.0: Design and Improvement of Rapid and Low-Cost Urine Analysis Device for Rural Health Care

Peecheck 2.0: Design and Improvement of Rapid and Low-Cost Urine Analysis Device for Rural Health Care

IoT Video Delivery Optimization through Machine Learning-Based Frame Resolution Adjustment

Providing acceptable video quality in the Internet of Things (IoT) implementation poses a significant challenge, mainly when the application is performed on low-cost and low-power devices. This research focuses on developing a frame resolution adjustment system that maintains the frame rate value of video delivery in wireless IoT environments with resource-constrained devices. Consistent frame rates prevent motion lag and data loss, improving user experience. The system works by predicting the upcoming throughput values using machine learning methods to adjust the sensing parameter, which is the resolution of the video frame to be captured by camera nodes. Hence, the proposed system is equipped with a file size estimator to estimate the size of the next video frame and then adjust the resolution in accordance with the throughput prediction. In this research, we conducted extensive experiments to evaluate the accuracy of the file size estimator and the throughput prediction. The experiment generated a dataset to evaluate throughput prediction and file size estimator model. The evaluation results for the file size estimator showed a mean absolute percentage error (MAPE) of 6.73% in the experiment using 317 frames with video resolutions between 72p and 720p. Experiments were also conducted to compare several machine learning methods for predicting throughput values. Compared to long short-term memory (LSTM) and autoregressive integrated moving average (ARIMA), simple exponential smoothing (SES) outperforms the others with the lowest root mean squared error (RMSE) and mean absolute error (MAE) values. Building upon these findings, we implemented the frame resolution adjustment system using SES as the method for predicting the upcoming throughput values. Finally, we demonstrated that the proposed system can maintain the frame rate according to the threshold set by the system while the resolution is being maximized, thereby addressing the challenges of maintaining video quality in resource-constrained IoT environments.

Interfacial Stabilization of Organic Electrochemical Transistors Conferred Using Polythiophene-Based Conjugated Block Copolymers with a Hydrophobic Coil Design.

The recent interest in developing low-cost, biocompatible, and lightweight bioelectronic devices has focused on organic electrochemical transistors (OECTs), which have the potential to fulfill these requirements. In this study, three types of poly(3-hexylthiophene) (P3HT)-based block copolymers (BCPs) incorporating different insulating blocks (poly(nbutyl acrylate) (PBA), polystyrene, and poly(ethylene oxide) (PEO)) were synthesized for application in OECTs. The morphological, crystallographic, and electrochemical properties of these BCPs are systematically investigated. Accordingly, P3HT-b-PBA demonstrates superior performance in the KCl-based aqueous electrolyte, with a higher product of mobility and capacitance (μC*) at 170 F s-1 cm-1 V-1 than that of the P3HT homopolymer at 58 F s-1 cm-1 V-1. P3HT-b-PBA exhibits better stability over 50 ON/OFF switching cycles than do other BCPs and P3HT homopolymers. With regard to the performance in the KPF6-based aqueous electrolyte, P3HT-b-PBA outperforms with a higher μC* of 9.2 F s-1 cm-1 V-1 than that of 8.6 F s-1 cm-1 V-1 observed from P3HT. Notably, both polymers exhibited almost no decay in device performance over 110 ON/OFF switching cycles. The strongly different performance of polymers in these two electrolytes is due to the side chain's hydrophobicity and interdigitated lamellar structures, thereby retarding the doping kinetics of the highly hydrated Cl- ions compared with the slightly hydrated PF6- ions. Concerning the improved performance of P3HT-b-PBA, this is attributed to its soft and hydrophobic backbone. Our morphological and crystallographic analyses reveal that P3HT-b-PBA experiences minimal structural disorder when swelled by the electrolyte, maintaining its original structure better than the P3HT homopolymer and the hydrophilic BCP of P3HT-b-PEO. The hydrophobic nature of P3HT-b-PBA contributes to the stability of its backbone structure, ensuring enhanced capacitance during the operation of the OECT operation. These findings provide reassurance about the stability and performance of P3HT-b-PBA in the field of OECT applications. In summary, this study represents the first exploration of P3HT-based BCPs for OECT applications and investigates their structure-performance relationships in mixed ionic-electronic conductors.