Low-cost Detection System Research Articles

SISTEMA PARA DETECÇÃO DE VAZAMENTO DE GÁS

Gas poisoning, such as Liquefied Petroleum Gas (LPG), is responsible for around 19,000 deaths annually, due to its high flammability and toxic effects when inhaled in large quantities(Science Advances, 2024). In response to this, a prototype low-cost LPGgas detection system was developed, based on Arduino. The system uses sensors to identify leaks and trigger alerts via LEDs, buzzer, fan and relay, an LCD board and an android app that receives the data sent viaBluetooth to the owner. Tests have confirmed the system’s efficiency in detecting dangerous levels of gas, with positive results for residential and commercial safety.

Design and Comparison of Low-Cost Urine Level Detection Systems for Critical Patients

Design and Comparison of Low-Cost Urine Level Detection Systems for Critical Patients

Standoff Identification of Plastic Waste Using a Low-Cost Compact Laser-Induced Breakdown Spectroscopy (LIBS) Detection System.

We report the standoff/remote identification of post-consumer plastic waste by utilizing a low-cost and compact standoff laser-induced breakdown spectroscopy (ST-LIBS) detection system. A single plano-convex lens is used for collecting the optical emissions from the plasma at a standoff distance of 6.5 m. A compact non-gated Czerny-Turner charge-coupled device (CCD) spectrometer (CT-CCD) is utilized to analyze the optical response. The single lens and CT-CCD combination not only reduces the cost of the detection system by tenfold, but also decreases the collection system size and weight compared to heavy telescopic-based intensified CCD systems. All the samples investigated in this study were collected from a local recycling plant. All the measurements were performed with only a single laser shot which enables rapid identification while probing a large number of samples in real time. Furthermore, principal component analysis has shown excellent separation among the samples and an artificial neural network analysis has revealed that plastic waste can be identified within ∼10 ms only (testing time) with accuracies up to ∼99%. Finally, these results have the potential to build a compact and low-cost ST-LIBS detection system for the rapid identification of plastic waste for real-time waste management applications.

Low-Cost Non-Wearable Fall Detection System Implemented on a Single Board Computer for People in Need of Care.

This work aims at proposing an affordable, non-wearable system to detect falls of people in need of care. The proposal uses artificial vision based on deep learning techniques implemented on a Raspberry Pi4 4GB RAM with a High-Definition IR-CUT camera. The CNN architecture classifies detected people into five classes: fallen, crouching, sitting, standing, and lying down. When a fall is detected, the system sends an alert notification to mobile devices through the Telegram instant messaging platform. The system was evaluated considering real daily indoor activities under different conditions: outfit, lightning, and distance from camera. Results show a good trade-off between performance and cost of the system. Obtained performance metrics are: precision of 96.4%, specificity of 96.6%, accuracy of 94.8%, and sensitivity of 93.1%. Regarding privacy concerns, even though this system uses a camera, the video is not recorded or monitored by anyone, and pictures are only sent in case of fall detection. This work can contribute to reducing the fatal consequences of falls in people in need of care by providing them with prompt attention. Such a low-cost solution would be desirable, particularly in developing countries with limited or no medical alert systems and few resources.

Development and application of a low-cost and portable multi-channel spectral detection system for mutton adulteration

It is important to develop low-cost, fast and portable meat adulteration detection systems to ensure the meat authenticity and safety in complex market environments. A multi-channel spectral detection system for meat adulteration was developed in this study. The core hardware of the system mainly includes a designed spectral module and a Raspberry pi controller. The spectral module consists of three multi-channel spectral sensors and LED lamps with specific wavelengths, containing 18 channels covering a range of 410–940 nm. The software was developed based on PyQt5. After completing the construction of the system, the detection distance was discussed and determined to be 4 mm. Based on the spectral data collected by the developed system, the models for classifying pure mutton, pure pork, mutton flavour essence adulteration, colourant adulteration and adulterated mutton with pork were established and compared. Four intelligent optimisation algorithms were further used to improve the model performance. The results of the test set showed that the support vector classification (SVC) model optimised by a sparrow search algorithm (SSA) obtained the best classification performance, with an accuracy of 97.59% and a Kappa coefficient of 0.9696. After the SSA-SVC was incorporated into the sensor software, the system performance was evaluated using external validation samples. The overall accuracy of the system was 94.29%. The system took about 5.31 s to detect a sample, and the total weight of the system was 1.55 kg. Overall, the developed portable spectral system has considerable potential to rapidly and accurately discriminate adulterated mutton in the field.

A low-cost UWB microwave imaging system for early-stage breast cancer detection

A low-cost UWB microwave imaging system for early-stage breast cancer detection

Pretreatment-free SERS sensing of microplastics using a self-attention-based neural network on hierarchically porous Ag foams

Low-cost detection systems are needed for the identification of microplastics (MPs) in environmental samples. However, their rapid identification is hindered by the need for complex isolation and pre-treatment methods. This study describes a comprehensive sensing platform to identify MPs in environmental samples without requiring independent separation or pre-treatment protocols. It leverages the physicochemical properties of macroporous-mesoporous silver (Ag) substrates templated with self-assembled polymeric micelles to concurrently separate and analyze multiple MP targets using surface-enhanced Raman spectroscopy (SERS). The hydrophobic layer on Ag aids in stabilizing the nanostructures in the environment and mitigates biofouling. To monitor complex samples with multiple MPs and to demultiplex numerous overlapping patterns, we develop a neural network (NN) algorithm called SpecATNet that employs a self-attention mechanism to resolve the complex dependencies and patterns in SERS data to identify six common types of MPs: polystyrene, polyethylene, polymethylmethacrylate, polytetrafluoroethylene, nylon, and polyethylene terephthalate. SpecATNet uses multi-label classification to analyze multi-component mixtures even in the presence of various interference agents. The combination of macroporous-mesoporous Ag substrates and self-attention-based NN technology holds potential to enable field monitoring of MPs by generating rich datasets that machines can interpret and analyze.

Low-cost monochromatic uniform illumination HSI system for detection and classification of apple bruise

Low-cost monochromatic uniform illumination HSI system for detection and classification of apple bruise

A compact microfluidic laser-induced fluorescence immunoassay system using avalanche photodiode for rapid detection of alpha-fetoprotein.

Alpha-fetoprotein (AFP), commonly employed for early diagnosis of liver cancer, serves as a biomarker for cancer screening and diagnosis. Combining the high sensitivity and specificity of fluorescence immunoassay (FIA), developing a low-cost and efficient immunoassay system for AFP detection holds significant importance in disease diagnosis. In this work, we developed a miniaturized oblique laser-induced fluorescence (LIF) immunoassay system, coupled with a microfluidic PMMA/paper hybrid chip, for rapid detection of AFP. The system employed an avalanche photodiode (APD) as the detector, and implemented multi-level filtering in the excitation light channel using the dichroic mirror and optical trap. At first, we employed the Savitzky-Golay filter and baseline off-set elimination methods to denoise and normalize the original data. Then the cutoff frequency of the low-pass filter and the reverse voltage of the APD were optimized to enhance the detection sensitivity of the system. Furthermore, the effect of laser power on the fluorescence excitation efficiency was investigated, and the sampling time during the scanning process was optimized. Finally, a four-parameter logistic (4PL) model was utilized to establish the concentration-response equation for AFP. The system was capable of detecting concentrations of AFP standard solution within the range of 1-500 ng/mL, with a detection limit of 0.8 ng/mL. The entire immunoassay process could be completed within 15 min. It has an excellent potential for applications in low-cost portable diagnostic instruments for the rapid detection of biomarkers.

Using Machine-Learning Technology to Implement a Nonhardware and Inexpensive Posture Detection System for Analyzing Body Posture Angles in Front Crawl Swimming

<p>Swimming is a sport that relies heavily on motor skills. Inability to maintain adequate bodily balance in water prevents swimmers from remaining afloat and propelling themselves. Due to the difficulty of attaching reflective stickers or LED (light-emitting diode) emitters to the body while adjusting the swimming posture, it is not possible to capture the posture like during a bicycle fitting; the refraction of water also affects the detection of the body’s posture. Addressing the shortcomings, this study developed a low-cost nonhardware posture detection system based on machine-learning models in MediaPipe. The system provides real-time and post analyses of posture angles and posture lines during front crawl swimming, thereby facilitating observation of the relationship between angle at which the arm enters the water and the body horizon. Two participants practicing front crawl were invited to test the proposed system. The experimental results confirmed that the proposed system provides effective detection and analyses of posture lines and angles in swimmers. The study also proposed the algorithm for optimizing posture angle detection to solve the problems of posture line distortion and angle calculation errors that arise when MediaPipe was used to detect a human skeleton above a water line. The system does not require the installation of hardware and is inexpensive to deploy, and it can be widely applied in front crawl swimming lessons to help learners adjust their arm’s entry angle and body horizon to reduce forward drag and increase speed.</p> <p> </p>

Iodine Gas Detection System Using Color Sensor

Iodine can exist in the form of radioactive and non-radioactive states. Radioactive Iodine-131 (I-131) has been utilized in nuclear facilities. Due to its ability to be absorbed by the thyroid gland, I-131 is used for therapy in thyroid cancer cases. Contrary to the benefit, Iodine is a volatile material that becomes a health problem if humans inhale more than a certain dose under a certain period. Thus, to prevent overexposure to Iodine, all Iodine-related facilities must equip their facilities with Iodine detection systems. The most common radioactive detection system uses an expensive Na(Tl), and HPGe detector. So, this study will build a low-cost iodine detection system by means of a low-cost color-based sensor utilizing the color property of the Iodine gasses. This finding showed that the color sensor will detect the Iodine gas leaks in the air in a range of 0 - 100 ppm, resulting in a pink-dark purple color. This finding showed that the sensor could detect low concentrations of Iodine gas in particular areas very well. Since the good resolution of this detection system, it will be promising to be applied in a real-time iodine gasses detection system.

A Low-Cost Instrumented Shoe System for Gait Phase Detection Based on Foot Plantar Pressure Data

A Low-Cost Instrumented Shoe System for Gait Phase Detection Based on Foot Plantar Pressure Data

Low-Cost LIDAR-Based Monitoring System for Fall Detection

Low-Cost LIDAR-Based Monitoring System for Fall Detection

Development of a Low-Cost Prototype System for Pipeline Operational and Vandalism Spillage Detection and Validation Framework

Development of a Low-Cost Prototype System for Pipeline Operational and Vandalism Spillage Detection and Validation Framework

Fire Detection System for Urban Structures with Integrated use of Multisensors in Embodied Technology

Objective: Create a sustainable, low-cost and effective fire detection system (FDS) for urban buildings, based on embedded technology, as an alternative to traditional detection systems. Method: experimental research that used the hypothetical-deductive method, with assemble hardware using the ATMEGA328P microcontroller and the MQ2 gas sensors, MLX90614 temperature and LM393 flame. When building the software, a qualitative model was created, based on everyday observations, associating it with a logical expression; Then, a quantitative model was created based on experimental data subjected to computational analysis using the decision tree method, defining a heuristic for early detection of fire patterns with precision. Results and conclusions: The suggested FDS had a shorter response time than the red ampoule sprinkler and an accuracy of 94%. This system proved to be more agile than common detectors, reducing water and energy consumption when fighting fires and the amount of resources used to replace affected assets. Furthermore, it has a low cost, making it accessible even at a residential. Implications of the research: It can be said that this research represents an important step in the construction of sustainable FDS, bringing social, economic and environmental benefits; and efficient due to the use of computational data analysis. Originality/Value: FDS which uses embedded technology and computational analysis to process fire variables, improves this analysis and allows for more agile and accurate detection, in addition to making the detection system cheaper and reducing the use of resources due to the fire.

A low-cost portable system for on-site detection of soil pH and potassium levels using 3D printed sensors

Accurate data on the nutrient content of soil is essential for optimum fertiliser application in agriculture. Frequent and regular soil tests hold the key to mitigate difficulties that arise from lack of accurate information for soil and crop management, such as increased costs for fertilisers, and environmental degradation. Traditionally soil nutrient contents levels are measured from laborious methods which involves collecting field samples and returning to the laboratory for analysis. Laboratory analysis often requires sample pre-treatment, chemical extractants and expensive instrumentation. A low-cost on-site detection method for soil nutrients can increase data necessary for smart fertiliser application that can in turn increase farm profit and ensure environmental protection. Here we present a low-cost miniaturised multi-ion sensing system for on-site detection of pH and potassium. The sensing system consists of a PSoC4 microcontroller and novel 3D printed miniaturised ion selective electrodes for the simultaneous determination of potassium and pH. The 3D printed electrode's pH sensing capability and potassium ion sensing was validated using known pH and potassium ion concentrations, exhibiting high selectivity and excellent Nernstian sensitivities for pH (−61.05 mV/pH) and potassium (49.50 mV/decade). Soil pore water samples from various locations and depths were extracted using a MacroRhizon sampler and tested using the novel portable detection system. On-site pH and potassium determinations were acquired in extremely small volumes samples without the requirement of sample preparation.

Development of a rapid, sensitive detection method for SARS-CoV-2 and influenza virus based on recombinase polymerase amplification combined with CRISPR-Cas12a assay.

Respiratory tract infections are associated with the most common diseases transmitted among people and remain a huge threat to global public health. Rapid and sensitive diagnosis of causative agents is critical for timely treatment and disease control. Here, we developed a novel method based on recombinase polymerase amplification (RPA) combined with CRISPR-Cas12a to detect three viral pathogens, including SARS-CoV-2, influenza A, and influenza B, which cause similar symptom complexes of flu cold in the respiratory tract. The detection method can be completed within 1 h, which is faster than other standard detection methods, and the limit of detection is approximately 102 copies/μL. Additionally, this detection system is highly specific and there is no cross-reactivity with other common respiratory tract pathogens. Based on this assay, we further developed a more simplified RPA/CRISPR-Cas12a system combined with lateral flow assay on a manual microfluidic chip, which can simultaneously detect these three viruses. This low-cost detection system is rapid and sensitive, which could be applied in the field and resource-limited areas without bulky and expensive instruments, providing powerful tools for the point-of-care diagnostic.

A fast permutation entropy for pulse rate variability online analysis with one-sample recursion.

Pulse rate variability (PRV) signals are extracted from pulsation signal can be effectively used for cardiovascular disease monitoring in wearable devices. Permutation entropy (PE) algorithm is an effective index for the analysis of PRV signals. However, PE is computationally intensive and impractical for online PRV processing on wearable devices. Therefore, to overcome this challenge, a fast permutation entropy (FPE) algorithm is proposed based on the microprocessor data updating process in this paper, which can analyze PRV signals with single-sample recursive. The simulation data and PRV signals extracted from pulse signals in "Fantasia database" were utilized to verify the performance and accuracy of the improved methods. The results show that the speed of FPE is 211 times faster than PE and maintain the accuracy of algorithm (Root Mean Squared Error=0) for simulation data with a length of 10,000 samples and embedded dimension m=5, time delay τ=5, buffer length Lw=512. For the RRV signals with 3000∼5000 samples, the result show that the consumption of FPE is less than 0.2s, which is 175 times faster than PE. This indicates that FPE has better application performance than PE. Furthermore, a low-cost wearable signal detection system is developed to verify the proposed method, the result show that the proposed method can calculate the FPE of PRV signal online with single-sample recursive calculation. Subsequently, entropy-based features are used to explore the performance of decision trees in identifying life-threatening arrhythmias, and the method resulted in a classification accuracy of 85.43%. It can therefore be inferred that the proposed method has great potential in cardiovascular disease.

LabVIEW based ECG simulator and automatic detection system for cardiac disorders

In the present age, most cardiac arrhythmias are emerging due to irregular rhythmic conditions of the heart. Diagnosis of cardiac arrhythmias plays an essential role in the survival of the human race. If rhythms of nature are distorted for any reason, it leads to several variations or abnormalities known as heart arrhythmias. Irregular heartbeats lead to anomalous P, QRS, and T values, which the patient’s ECG can follow. The growing well-being concerns, especially for cardiac arrhythmias, reflect the requirement of creating a cheap and convenient ECG detection system. The most common heart conditions are tachycardia, bradycardia, atrial fibrillation, myocardial infarction and many other cardiac disorders. These diseases are one of the most significant causes of death worldwide. There is much research for recognizable proof of cardiovascular diseases utilizing ECG diagnostic toolbox and different software. So, in this project, the idea is to design an Automatic Disease Detection System on LabVIEW to detect various cardiac abnormalities such as Atrial Flutter (AF), Supraventricular Tachycardia (SVT), Left Bundle Branch Block (LBBB), Ventricular Fibrillation (VF), Myocardial Infarction (MI), Hyperkalaemia and Digoxin by comparing it to the normal ECG signal. So for this, NI LABVIEW (2018) software has been used for designing. This Automated Disease Detection System, hopefully, gives the best accurate and fast results which are low cost, effective, efficient, easy handled and high accuracy rate detection system which can be used in hospitals for medical professionals.

Slope displacement detection in construction: An automated management algorithm for disaster prevention

In construction sites, embankment slope failure has led to catastrophic events, necessitating advanced monitoring for disaster prevention. This study presents a novel Slope Displacement Inspection and Management Algorithm (SDIMA) that utilizes Global Navigation Satellite System (GNSS) data to detect significant slope displacements automatically. SDIMA analyzes GNSS data to discern displacements in various directions (North-South, East-West, Vertical) and separate actual change points from measurement errors by employing a robust mathematical model called Displacement Detector. It successfully detected change points with displacements greater than two times the samples' standard deviation, and thresholds were set for displacements ≥ 25 mm. A comparative analysis showed promising efficiency in North-South detection, while certain limitations were identified. The proposed SDIMA outperforms existing models, achieving a Mean Absolute Error (MAE) of 3.157 mm, Root Mean Square Error (RMSE) of 4.754 mm, Mean Absolute Scaled Error (MASE) of 0.294, and an average processing time of 0.578 min, making it a highly efficient and accurate expert system. The innovation of SDIMA lies in its ability to provide an automated decision support tool for site managers, laying the foundation for a low-cost anomaly detection system for slope failure disaster prevention. The algorithm's proficiency and its potential to integrate with existing practices herald a significant advancement in construction management and safety.