Low-cost System Research Articles

Demonstration of GaN HEMT with Cu-based Ti/TiN/Cu Ohmic metal and TiN/Cu gate metal: comparison with Au-based device

Abstract In this study, GaN high electron mobility transistor (HEMT) on silicon substrate was utilized Cu-based metal for the ohmic contacts (Source and Drain terminals) and the Schottky contact (Gate terminal) as part of the metallization process. Furthermore, the same epi-wafer was used in this study to fabricate Au-based HEMTs, which served as control samples. Transmission electron microscopy (TEM) analysis comparing Ti/TiN/Cu ohmic contacts of Cu-based HEMT to Control sample. The Au-based Ohmic contact forms rough surfaces, discrete TiN islands, and defects in the AlGaN layer, potentially degrading device reliability. The Cu-based Ohmic contact features a smooth surface, minimal defects in the AlGaN layer, a stable Ti/TiN interface, and effective prevention of Cu diffusion, enhancing device reliability and scalability. The specific contact resistance (ρ c) of the Cu-based and Au-based ohmic contact metals produced in this study were 6.68 × 10−6 Ω-cm2 (2.04 Ω mm−1) and 9.64 × 10−6 Ω-cm2 (2.06 Ω mm−1), respectively. In addition, compared with the Cu-based and Au-based HEMT components in this study, the Cu-based HEMT, which used TiN/Cu Gate metal, exhibited excellent electrical characteristics (IDS: 1023 mA mm−1, Gm: 570 mS mm−1). Through reliability testing, it was confirmed that the Cu-based Gate metal does not cause Vth shift or affect IDS. The fT and fmax of the Cu-based HEMT were 33.0 GHz and 99.6 GHz, respectively, which were 12.6 GHz (61.8%) and 26.1 GHz (35.5%) higher than the fT and fmax of the control sample (Au-based HEMT). This increase demonstrated that the Cu-based HEMT achieved higher switching speeds, enhancing its suitability for high-frequency applications. The Cu-based HEMT components achieved the same characteristics as Au-based HEMT components under frequency measurements. In future development of low-cost systems, copper-based HEMT components will play an important economic role.

Design and performance analysis of a low-cost monitoring system for advanced building envelopes

The objective of this study is to design a low-cost system for long-term monitoring of advanced building envelope and evaluates its performance in comparison to conventional lab-grade sensors. Leveraging the potentials of a single-board computer and several low-cost digital sensors, the system measured thermo-physical and environmental parameters, including temperature, humidity, CO2 levels, airflow rate, lighting, and heat flux. The system's architecture allowed for easy integration and flexible connectivity, providing comprehensive monitoring capabilities at a fraction of the cost of lab-grade systems. A basic prototype is available in a GitHub repository. The systematic evaluation of the proposed concept involved three key experiments using a double-skin façade mockup installed in a full-scale climate simulator. Sensor accuracy over 24 hours was assessed through time-series comparison, showing high accuracy in recording air temperature, humidity, and surface temperature without on-site calibration, though calibration was essential for precise CO2 and lighting measurements. Key performance indicators of the thermophysical behavior of envelope systems, such as U-value and g-value, were derived for both the low-cost and lab-grade setups. Discrepancies of up to 7% in U-value and 13% in g-value were observed, confirming the system's reliability for building energy assessments. Additionally, the low-cost system effectively represented dependencies between independent and dependent variables, as shown by analysis of variance, closely aligning with the picture obtained from lab-grade sensors’ data. These findings validate the low-cost monitoring system as a viable, cost-effective alternative for advanced building envelope performance studies and indoor environmental quality assessments, highlighting the need for calibration of certain sensors.

Low-cost integrated circuit packaging defect classification system using edge impulse and ESP32CAM

Defects in integrated circuit (IC) packaging are inevitable. Several factors can cause defects in IC packaging such as material quality, errors in machine and human handling operations, and non-optimized processes. An automated optical inspection (AOI) is a typical method to find defects in the IC manufacturing field. Nevertheless, AOI requires human assistance in the event of uncertain defect classification. Human inspection often misses very tiny defects and is inconsistent throughout the inspection. Therefore, this study proposed a low-cost IC packaging defect classification system using edge impulse and ESP32-CAM. The method involves training a deep learning model (i.e., convolutional neural network (CNN)) using a dataset of non-defective and defective ICs on Edge Impulse. For defective ICs, the top surface of the ICs is deliberately scratched to imitate the cosmetic defects. ICs with scratch-free on their top surfaces are considered non-defective ICs. A successfully trained model using Edge Impulse is subsequently deployed on ESP32-CAM. The model is optimized to fit the limited resources of the ESP32-CAM. By using the built-in camera in ESP32-CAM, the trained model can perform a real-time image classification of non-defective/defective ICs. The proposed system achieves 86.1% prediction accuracy by using a 1,571 image dataset of defective and non-defective ICs.

Hydrophobic modification of cellulose nanofibers/bionic flower-like ZnO synergistically stabilized Pickering emulsion to enhance pesticide deposition.

Environmental issues arising from the low pesticide utilization rate make the development of environmentally friendly and low-cost pesticide carrier systems an urgent problem to be solved. Pickering emulsion systems have shown broad application prospects in pesticide delivery. In this study, dodecenyl succinic anhydride (DDSA) was used to hydrophobically modify cellulose nanofiber (D-CNF), and biomimetic flower-like zinc oxide (ZnO) particles were prepared by precipitation method at room temperature. Then the two functioned synergistically as stabilizers for the Pickering emulsion, which significantly diminished the reliance on conventional surfactants. Turpentine, an essential oil, was used as a solvent for the broad-spectrum herbicide 2,4-dichlorophenoxyacetic acid (2,4-D) and as a part of the oil phase in an emulsion, replacing traditional toxic solvents for pesticide dissolution in the construction of a Pickering emulsion system. ZnO/D-CNF formed a dense layer at the oil-water interface to prevent droplet aggregation. After the Pickering emulsion contacted the leaves, ZnO could be embedded and adhere to leaves due to its morphology, which effectively reduced the splash of the droplets. No matter what the inclination angle was, the droplets could adhere and diffuse on the leaves. This study presents a new idea for creating a simple, eco-friendly, and practical pesticide carrier system.

Impacts of HVAC cleaning on energy consumption and supply airflow: A multi-climate evaluation

Energy-efficiency interventions are crucial for sustainable building operations to accommodate emerging indoor air quality (IAQ) criteria into their engineering life cycles. While several studies have addressed building energy consumption and IAQ considerations separately, few provide integrated analysis of these aspects in response to building hygiene practices. In response, this study evaluates the effectiveness of routine heating, ventilation, and air conditioning (HVAC) cleaning on energy consumption and supply airflow patterns in non-residential public buildings. This study juxtaposes HVAC energy consumption and ventilation performance before, during and after routine HVAC cleaning, across buildings situated in four different climate zones, while operating in cooling mode. Each site had nearly identical HVAC systems serving similar architectural features and occupational loads; these were segregated into an intervention (cleaned HVAC system) that could be compared to an otherwise identically operating HVAC (control system), which was not cleaned. Following prescriptive cleaning, HVAC systems exhibited significant energy consumption reductions and delivered higher airflows compared to their uncleaned counterparts. On average, intervention systems saved between 41 % and 60 % on conveyance (fan/blower) energy, with one exception, and supplied 10 % and 46 % more airflow compared to their uncleaned counterparts. This research demonstrates how a new generation of low-cost HVAC system monitors can compile Internet of Things (IoT) archives to show immediate energy consumption benefits associated with cleaning HVAC components and their associated ductwork serving relatively high occupancy commercial and educational spaces.

From concept to application: building and testing a low-cost light detection and ranging system for small mobile robots using time-of-flight sensors

Advancements in light detection and ranging (LiDAR) technology have significantly improved robotics and automated navigation. However, the high cost of traditional LiDAR sensors restricts their use in small-scale robotic projects. This paper details the development of a low-cost LiDAR prototype for small mobile robots, using time-of-flight (ToF) sensors as a cost-effective alternative. Integrated with an ESP32 microcontroller for real-time data processing and Wi-Fi connectivity, the prototype facilitates accurate distance measurement and environmental mapping, crucial for autonomous navigation. Our approach included hardware design and assembly, followed by programming the ToF sensors and ESP32 for data collection and actuation. Experiments validated the accuracy of the ToF sensors under static, dynamic, and varied lighting conditions. Results show that our low-cost system achieves accuracy and reliability comparable to more expensive options, with an average mapping error within acceptable limits for practical use. This work offers a blueprint for affordable LiDAR systems, expanding access to technology for research and education, and demonstrating the viability of ToF sensors in economical robotic navigation and mapping solutions.

Development of next generation low-cost OCT towards improved point-of-care retinal imaging

Low-cost optical coherence tomography (OCT) has shown promise in increasing access to noninvasive retinal imaging at the point of care, especially in low-resource environments. A next-generation low-cost OCT system is presented which improves performance over previous versions by employing balanced detection, improved spectrometer falloff, and an increased A-line rate of 40 kHz. An algorithm is presented for image display that uses a histogram matching procedure to improve contrast-to-noise ratio (CNR). Imaging performance is benchmarked with CNR analysis of retinal OCT images, demonstrating a CNR of 2.01 ± 0.39 (p < 0.0001) for macula images collected during a clinical trial, a significant improvement over previous low-cost OCT systems.

Generating Seamless Three-Dimensional Maps by Integrating Low-Cost Unmanned Aerial Vehicle Imagery and Mobile Mapping System Data

Generating Seamless Three-Dimensional Maps by Integrating Low-Cost Unmanned Aerial Vehicle Imagery and Mobile Mapping System Data

Upgrading a Low-Cost Seismograph for Monitoring Local Seismicity

The use of a dense network of commercial high-cost seismographs for earthquake monitoring is often financially unfeasible. A viable alternative to address this limitation is the development of a network of low-cost seismographs capable of monitoring local seismic events with a precision comparable to that of high-cost instruments within a specified distance from the epicenter. The primary aim of this study is to compare the performance of an advanced, contemporary low-cost seismograph with that of a commercial, high-cost seismograph. The proposed system is enhanced through the integration of a 24-bit analog-to-digital converter board and an optimized architecture for a low-noise signal amplifier employing active components for seismic signal detection. To calibrate and assess the performance of the low-cost seismograph, an installation was deployed in a region of high seismic activity in Evgiros, Lefkada Island, Greece. The low-cost system was co-located with a high-resolution 24-bit commercial digitizer, equipped with a broadband (30 s—50 Hz) seismometer. An uninterrupted dataset was collected from the low-cost system over a period of more than two years, encompassing 60 local events with magnitudes ranging from 0.9 to 3.2, epicentral distances from 5.71 km to 23.45 km, and focal depths from 1.83 km to 19.69 km. Preliminary findings demonstrate a significant improvement in the accuracy of earthquake magnitude estimation compared to the initial configuration of the low-cost seismograph. Specifically, the proposed system achieved a mean error of ±0.087 when benchmarked against the data collected by the high-cost commercial seismograph. These results underscore the potential of low-cost seismographs to serve as an effective and financially accessible solution for local seismic monitoring.

Colloidal Quantum-Dot Heterojunction Imagers for Room-Temperature Thermal Imaging.

Room-temperature operation or high-operation temperature (HOT) is essential for mid-wave infrared (MWIR) optoelectronics devices providing low-cost and compact systems for numerous applications. Colloidal quantum dots (CQDs) have emerged as a rising candidate to enable photodetectors to operate at HOT or room temperature and develop the next-generation infrared focal plane array (FPA) imagers. Here, band-engineered heterojunctions are demonstrated to suppress dark current with well-passivated mercury telluride (HgTe) CQDs enabling room-temperature MWIR imaging by single-pixel scanning and 640×512 FPA sensitive thermal imaging above 250K. As a result, the room-temperature detectivity reaches as high as 1.26×1010 Jones, and the noise equivalent temperature difference (NETD) is as good as 25mK up to 200K.

Towards a low-cost and privacy-preserving indoor activity recognition system using wifi channel state information

Towards a low-cost and privacy-preserving indoor activity recognition system using wifi channel state information

Single-pixel computational spectrometer based on nonlinear spectrum modulation

Computational spectroscopy with modulated light spectrum is especially well-suited for application scenarios where active illumination is needed. Most of the existing computational spectroscopy, however, uses ambient illumination and modulates the signal spectrum that is reflected or transmitted from objects, mainly because of the lack of a light source with effective spectrum modulation. Here, we present a novel computational spectrometer featuring light-source spectrum modulation achieved through polarization-induced nonlinear spectrum modulation, offering a compact and low-cost system apparatus. The modulated spectrum can evolve periodically by managing polarization-induced nonlinear phase as well as pump power, within the range from 1000 nm to 1100 nm. Combining wavelength multiplexing and compressed sensing, we can achieve a minimum spectral resolution of 0.2 nm, which is comparable to the commercial spectrometer. The utilized objects with sparse and non-sparse spectra are successfully reconstructed both in simulation and experiment, within an effective reconstruction spectral range from 1021 nm to 1077 nm.

One-Point Calibration of Low-Cost Sensors for Particulate Air Matter (PM) Concentration Measurement

The use of low-cost sensors has dramatically increased in recent years in all engineering sectors. In the buildings and automotive field, low-cost sensors open very interesting perspectives, because they allow one to monitor temperature and humidity distributions together with air quality in a widespread and punctual way and allow for the control of all energy parameters. The main issue remains the validation of the measurements. In this work, we propose an innovative approach to verify the measurements given by some low-cost systems built ad hoc for automotive applications. Two independent low-cost measurement systems were set to measure Particulate Air Matter (PM) concentration, TVOC concentration, CO2 concentration, formaldehyde concentration, air temperature, relative humidity, pressure, air flow velocity, and GPS position. These systems were calibrated for PM concentration measurement by comparison with standard and certified sensors used by the regional authority of the Emilia-Romagna region (ARPAE, Italy) for characterizing air quality. The duration of the analysis, three days, is not representative of the diverse environmental conditions that occur across different seasons. However, the innovation of this approach lies in both the in-field comparison of low-cost and high-quality sensors and the use of proper conversion approaches for mass concentration measurements. A quantitative analysis of the sensors’ performance is given, with a focus on the effects of time granularity, relative humidity, mass conversion from particle counts, and size detection response. The results show that the low-cost sensors’ measurements of air temperature, relative humidity, and particle number concentration are in good agreement with high-quality sensors’ measurements, with a strong impact of relative humidity on performance indicators. Overall, good quality and consistency of the data among the sensors were achieved.

An open-source GIS app for digitalising bridge inspections and automating BIM model integration

Ensuring the safety and functionality of civil infrastructure, especially bridges, is critical for sustainable transportation networks. To streamline maintenance operations, on-site inspections are periodically conducted. The routine inspections of bridges frequently depend on manual processes, which diminish efficiency and data accessibility, leading to errors that jeopardise accurate decision-making. This paper advocates for the digitalisation to address these issues, developing a low-cost Geographical Information System (GIS)-based app for streamlined bridge inspections. The digital protocol automatically transfers georeferenced on-site inspection information into the bridge’s Building Information Model (BIM) using a parametric programming tool. This integration aims to improve decision-making in maintenance operations. The developed approach is validated through a real bridge inspection of a concrete bridge in Spain.

Regulation of Zinc Hydroxide Sulfate Growth Environment for Stable Zinc Anode/Electrolyte Interfaces.

Metallic Zn is a promising anode for high-safety, low-cost, and large-scale energy storage systems. However, it is strongly hindered by unstable electrode/electrolyte interface issues, including zinc dendrite, corrosion, passivation, and hydrogen evolution reactions. In this work, an in situ interface protection strategy is established by turning the corrosion/passivation byproducts (zinc hydroxide sulfates, ZHSs) into a stable hybrid protection layer. The hydrolysis of the diglycolamine buffer layer on the zinc anode provides a homogeneous basic electrolyte environment for the generation of small-sized ZHS, thereby leading to the formation of a ZHS-based hybrid layer. Benefiting from this hybrid layer, uniform zinc ion flux and high anticorrosion ability can be achieved. As a result, the decorated symmetric cell presents a long cycling lifespan of over 1500 h at a current density of 1 mA cm-2 and an area capacity of 1 mAh cm-2. It also contributes to the appealing cycling and rate performance of Zn|NH4V4O10 full cells. This work provides insight into regulating and reusing interfacial byproducts for high-performance zinc metal batteries.

Low-Cost Embedded System Applications for Smart Cities

The Internet of Things (IoT) represents a transformative technology that allows interconnected devices to exchange data over the Internet, enabling automation and real-time decision making in a variety of areas. A key aspect of the success of the IoT lies in its integration with low-resource hardware, such as low-cost microprocessors and microcontrollers. These devices, which are affordable and energy efficient, are capable of handling basic tasks such as sensing, processing, and data transmission. Their low cost makes them ideal for IoT applications in low-income communities where the government is often absent. This review aims to present some applications—such as a flood detection system; a monitoring system for analog and digital sensors; an air quality measurement system; a mesh video network for community surveillance; and a real-time fleet management system—that use low-cost hardware such as ESP32, Raspberry Pi, and Arduino, and the MQTT protocol used to implement low-cost monitoring systems applied to improve the quality of life of people in small cities or communities.

Enhancing Cross-Modal Camera Image and LiDAR Data Registration Using Feature-Based Matching

Registering light detection and ranging (LiDAR) data with optical camera images enhances spatial awareness in autonomous driving, robotics, and geographic information systems. The current challenges in this field involve aligning 2D-3D data acquired from sources with distinct coordinate systems, orientations, and resolutions. This paper introduces a new pipeline for camera–LiDAR post-registration to produce colorized point clouds. Utilizing deep learning-based matching between 2D spherical projection LiDAR feature layers and camera images, we can map 3D LiDAR coordinates to image grey values. Various LiDAR feature layers, including intensity, bearing angle, depth, and different weighted combinations, are used to find correspondence with camera images utilizing state-of-the-art deep learning matching algorithms, i.e., SuperGlue and LoFTR. Registration is achieved using collinearity equations and RANSAC to remove false matches. The pipeline’s accuracy is tested using survey-grade terrestrial datasets from the TX5 scanner, as well as datasets from a custom-made, low-cost mobile mapping system (MMS) named Simultaneous Localization And Mapping Multi-sensor roBOT (SLAMM-BOT) across diverse scenes, in which both outperformed their baseline solutions. SuperGlue performed best in high-feature scenes, whereas LoFTR performed best in low-feature or sparse data scenes. The LiDAR intensity layer had the strongest matches, but combining feature layers improved matching and reduced errors.

SOLAR TRACKING SYSTEM USING ARDUINO

This research focuses on the development of a solar tracking system using an Arduino microcontroller to optimize the energy efficiency of photovoltaic solar panels. The conventional fixed solar panel setup often leads to suboptimal energy capture as the panel’s orientation remains static, resulting in reduced efficiency due to the sun’s changing position throughout the day. To address this, the proposed system employs a dual-axis solar tracker mechanism that automatically adjusts the solar panel's orientation to align with the sun's trajectory, maximizing solar energy absorption. The system utilizes Light Dependent Resistors (LDRs) placed on either side of the solar panel to detect variations in light intensity. Based on the sensor readings, the Arduino microcontroller processes the information and drives servo motors to move the panel in real time, ensuring it faces the sun. The servo motors allow for precise angular adjustments, enabling both horizontal and vertical movement to track the sun across the sky. The Arduino-based control system is simple, cost-effective, and easily programmable, offering an accessible solution for enhancing solar power efficiency. A comparative analysis between the solar tracker and a fixed-panel setup shows that the tracking system significantly improves energy capture by maintaining optimal sunlight exposure throughout the day. The paper also discusses the challenges involved in system calibration, the reliability of sensor data, and the mechanical aspects of the tracking mechanism. Results demonstrate that the solar tracking system can increase energy output by up to 30% compared to static panels. The findings indicate that the proposed low-cost solar tracking system holds significant potential for large-scale solar applications, contributing to the increased adoption of renewable energy solutions and enhancing the overall performance of solar power systems. Experimental evaluations were conducted to compare the energy output of the solar tracker against a fixed-panel setup. The results indicate a significant improvement in energy generation with the tracking system. The solar tracker’s ability to follow the sun maximizes light absorption and increases the efficiency of the photovoltaic panel by up to 30% compared to a fixed panel. The system demonstrates notable advantages, including higher energy yields, better adaptability to changing weather conditions, and the ability to capture sunlight at different times of the day. Key Words: Solar Tracker, Light Detecting Resistor (LDR), Arduino, Servo Motor

Depth-Oriented Gray Image for Unseen Pig Detection in Real Time

With the increasing demand for pork, improving pig health and welfare management productivity has become a priority. However, it is impractical for humans to manually monitor all pigsties in commercial-scale pig farms, highlighting the need for automated health monitoring systems. In such systems, object detection is essential. However, challenges such as insufficient training data, low computational performance, and generalization issues in diverse environments make achieving high accuracy in unseen environments difficult. Conventional RGB-based object detection models face performance limitations due to brightness similarity between objects and backgrounds, new facility installations, and varying lighting conditions. To address these challenges, this study proposes a DOG (Depth-Oriented Gray) image generation method using various foundation models (SAM, LaMa, Depth Anything). Without additional sensors or retraining, the proposed method utilizes depth information from the testing environment to distinguish between foreground and background, generating depth background images and establishing an approach to define the Region of Interest (RoI) and Region of Uninterest (RoU). By converting RGB input images into the HSV color space and combining HSV-Value, inverted HSV-Saturation, and the generated depth background images, DOG images are created to enhance foreground object features while effectively suppressing background information. Experimental results using low-cost CPU and GPU systems demonstrated that DOG images improved detection accuracy (AP50) by up to 6.4% compared to conventional gray images. Moreover, DOG image generation achieved real-time processing speeds, taking 3.6 ms on a CPU, approximately 53.8 times faster than the GPU-based depth image generation time of Depth Anything, which requires 193.7 ms.

Departing from standard practices: Strategic application of value engineering in the anatomy laboratory to enhance formaldehyde extraction using high-impact, low-cost, and low-maintenance solutions.

This study describes the process of developing a high-impact, low-cost, and low-maintenance air ventilation system for anatomy facilities. It employed the strategic application of Value Engineering (VE), assuring that the air ventilation system meets contemporary threshold limit values (TLVs) for formaldehyde in the working zone of dissection tables. A creative-innovative construction methodology was used, combining the Theory of Inventive Problem Solving (TRIZ/TIPS) and VE for an anatomy laboratory air ventilation concept. The TRIZ/TIPS aimed to resolve conflicts that impeded progress toward higher ideality, while VE aimed to develop alternative approaches to fulfill required functions at a minimal cost. The findings were first trialed in a mockup while dissecting human tissues embalmed with two protocols. The experimental results were validated by computational fluid dynamics simulations, and then followed by a pilot and commissioning phase once the physical installation of the dissection laboratory concluded. The findings demonstrate the superiority of the combined TRIZ/TIPS and VE approach in terms of air distribution and efficient formaldehyde extraction within the breathing zone. A formaldehyde exposition below 0.1 ppm, lowered air exchange rates, and system usability proved that the given third-generation ventilation system complies with contemporary TLVs and potential changes in user requirements. The third-generation ventilation system offers a cost-effective, high-impact, and low-maintenance solution for state-of-the-art air ventilation systems in the anatomy dissection laboratory. The underlying design approach ensured that formaldehyde levels in the laboratory meet the TLV and indoor air guideline values for formaldehyde exposure, at which current knowledge indicates no increased risk of cancer.