Automatic Detection Device Research Articles

ATD-300 an AI empowered miniature acoustic device for automatic gunshot attacks detection and image capture

Built on over 30 years of acoustic source recognition expertise in transient impulsives signature, recently highly empowered by neural networks, and wave propagation model driven algorithms, ACOEM has developed the ATD-300, an acoustic sensor for automatic gunshot detection, identification and localization. What sets this sensor apart from the competition is its reliability in terms of detection rate over false alarm and robust high resolution shooter pointing capability. Therefore it can be used directly by police departments without third-party human validation. Resulting from the design effort to distribute intelligence among a collaborative sensor network, processing and artificial intelligence algorithms are fully embedded locally, enabling an alert to be raised in less than three seconds and a camera to be pointed at the shooter with only one sensor. In addition, as the system is already installed in several American cities, we will show how a device-to-server distributed artificial intelligence approach continuously improves the performance of the solution, fed by real field data.

Pembuatan Prototype Pendeteksi Suhu dan Disinfektan Otomatis Pada Laboratorium

When the pandemic occurred in 2019, disinfectant sprayers were needed for prevention, measuring body temperature at the entrance. The pandemic has been end, we still have to do this to prevent and be disciplined in healthy living. The research here will create a prototype of an automatic temperature detection and disinfectant device at the laboratory door. The laboratory location was chosen because mobility in the laboratory was conducive, if a trial was carried out it would not disturb the activity on laboratory. This spraying tool designed includes an HC-SR04 sensor for detecting the height of disinfectant liquid, a TCRT 5000 sensor for detecting a person's forehead which will take temperature measurements to the MLX90614 sensor. The buzzer sounds when the MLX90614 sensor detects a temperature above 370C. Spraying disinfectant liquid uses 2 0.2mm spayer nozzles, and a proximity sensor when detecting people passing by the spray booth.

Evaluating machine learning algorithm for real-time heat exchanger optimization and automatic issue detection device: experimental analysis

Evaluating machine learning algorithm for real-time heat exchanger optimization and automatic issue detection device: experimental analysis

Portable automatic detection system with infrared imaging for measuring steel wires corrosion damage

Accurate, cost-efficient and lightweight quantitative detection of steel wire corrosion in cable is an unsolvable issue that puzzles researchers and bridge owners. This paper proposed a portable automatic detection system for quantitative corrosion detection of replaced steel wires. The developed system consists of three parts (an automatic detection device, high-precision infrared imager, and corrosion evaluation algorithm). The proposed method is derived on the basis of Joule's law and the formula for calculating heat release. Then, the corroded specimens with different corrosion degrees and corresponding mass loss ratio were obtained by pre-corroded test to simulate industrial air corrosion and pickling test. The prior knowledge acquisition test utilizing presented automatic detection equipment was conducted to acquire the surface temperature of steel wires with various corrosion ratios by infrared camera after electric heating. Subsequently, the optimal quantitative corrosion prediction model after electric excitation is selected after infrared image processing, outlier filtering, theoretical method comparison, and model modification. Finally, the damaged steel wires from an in-service cable-stayed bridge are chosen to validate the proposed model's generalization ability and predictive accuracy. Furthermore, based on the discovery of characteristics on surface temperature of corroded steel wires after heating, the automatic detection device can be upgraded to realize the quantitative detection of in-situ corrosion of bridge cable.

The spectrum of eating environments encountered in free living adults documented using a passive capture food intake wearable device.

The aim of this feasibility and proof-of-concept study was to examine the use of a novel wearable device for automatic food intake detection to capture the full range of free-living eating environments of adults with overweight and obesity. In this paper, we document eating environments of individuals that have not been thoroughly described previously in nutrition software as current practices rely on participant self-report and methods with limited eating environment options. Data from 25 participants and 116 total days (7 men, 18 women, Mage = 44 ± 12 years, BMI 34.3 ± 5.2 kg/mm2), who wore the passive capture device for at least 7 consecutive days (≥12h waking hours/d) were analyzed. Data were analyzed at the participant level and stratified amongst meal type into breakfast, lunch, dinner, and snack categories. Out of 116 days, 68.1% included breakfast, 71.5% included lunch, 82.8% included dinner, and 86.2% included at least one snack. The most prevalent eating environment among all eating occasions was at home and with one or more screens in use (breakfast: 48.1%, lunch: 42.2%, dinner: 50%, and snacks: 55%), eating alone (breakfast: 75.9%, lunch: 89.2%, dinner: 74.3%, snacks: 74.3%), in the dining room (breakfast: 36.7%, lunch: 30.1%, dinner: 45.8%) or living room (snacks: 28.0%), and in multiple locations (breakfast: 44.3%, lunch: 28.8%, dinner: 44.8%, snacks: 41.3%). Results suggest a passive capture device can provide accurate detection of food intake in multiple eating environments. To our knowledge, this is the first study to classify eating occasions in multiple eating environments and may be a useful tool for future behavioral research studies to accurately codify eating environments.

Research and Development of Intelligent Cleaning and Mud Content Detection Technology for Machine-Made Sand

In this paper, an improved method of sand and mud content detection for concrete is proposed, and a set of sand and mud content detection device for concrete is designed. In this paper, a concrete gas content meter and a bubble parameter analysis system were used to study the effect of sand clay content on the gas content of commercial concrete, the loss rate of gas content in warp time, the bubble spacing coefficient and the distribution of hardened appearance pores. The pore structure test shows that the increase of mud content will appropriately reduce the proportion of harmless pores in machine-made sand concrete, and play a role in refining the pore structure in machine-made sand concrete. By using PLC controller and automatic detection device of liquid level and turbidity, the problems of complicated operation, inconvenient carrying and poor repeatability of test data are solved. It reduces the influence of human operation error and improves the accuracy of test data.

3D printed bionic self-powered sensing device based on fern-shaped nitrogen doped BiVO4 photoanode with enriched oxygen vacancies

A portable three-dimensional (3D) printed bionic sensing device with enhanced photoelectric response was fabricated for sensitive detection of Bisphenol A (BPA). The proposed sensor is operated upon by using a highly reactive dual-electrode system to generate electrical output and provide the sensing signal under photoirradiation, without an external power source. The fern-shaped nitrogen doped BiVO4 photoanode with enriched oxygen vacancies (Ov) bismuth vanadate (N/Ov/BiVO4) photoanode was first synthesized and applied to construct a bionic sensing device. Density functional theoretical (DFT) calculation shows that the synergistic of nitrogen doping and Ov on the surface of photoanode leads to the emergence of impurity levels in BiVO4’s electronic structure, promoting the effective separation of photogenerated electron-hole pairs. Impressively, the unique fern-shaped bionic structure enhances the mass transfer efficiency of the sensing system and provides abundant binding sites of aptamer, realizing signal amplification. Moreover, a portable sensing device for automatic sample injection and detection is developed by integrating the detection system into a micromodel based on micro-nano 3D printing technology. Benefit from this ingenious design, the proposed bionic aptasensor displayed excellent electricity output and achieved high sensitivity and selectivity of BPA detection with a low limit of detection (0.025 nM) and broad linear range from 0.1 nM to 100 μM, paving a new way for the development of portable and on-site sensing devices.

Application of near infrared spectroscopy and real time release testing combined with statistical process control charts for on-line quality control of industrial concentrating process of traditional Chinese medicine “Jinyinhua”

Concentrating process is an important unit operation during the manufacture of Lonicerae Japonicae Flos (LJF, “Jinyinhua” in Chinese) products. Near infrared (NIR) spectroscopy and real time release testing (RTRT) combined with statistical process control (SPC) methods were presented for on-line monitoring of concentrating and developing quality control strategy for the final LJF concentrates. An automatic NIR detection (AND) device was designed and has proven effective to eliminate the influence of bubble, solid impurity and flow of the concentrates on NIR spectra. Partial least squares (PLS) models were constructed for on-line determination of neochlorogenic acid (NCA), chlorogenic acid (CA), cryptochlorogenic acid (CCA), density and water content, which provided real-time data and exhibited satisfactory predictive abilities. SPC tools, including univariate Shewhart, multivariate Hotelling T2 and squared prediction error (SPE), were compared and applied for RTRT. The results of RTRT showed that the releasing criteria for final LJF concentrates were as follows: 1.09 mg/g < NCA < 1.53 mg/g, 10.00 mg/g < CA < 13.44 mg/g, 1.71 mg/g < CCA < 2.43 mg/g, 1.10 g/ml < density < 1.14 g/ml, 64.07% < water content < 72.99% and SPE result < 0.04. Shewhart and SPE charts have proven to be useful in detecting abnormalities and releasing normal batches. This work demonstrated the effectiveness of NIR and RTRT combined with SPC charts in on-line quality control of industrial concentrating process of traditional Chinese medicine.

Commercially Available Heart Rate Monitor Repurposed for Automatic Arrhythmia Detection with Snapshot Electrocardiographic Capability: A Pilot Validation.

The usefulness of opportunistic arrhythmia screening strategies, using an electrocardiogram (ECG) or other methods for random “snapshot” assessments is limited by the unexpected and occasional nature of arrhythmias, leading to a high rate of missed diagnosis. We have previously validated a cardiac monitoring system for AF detection pairing simple consumer-grade Bluetooth low-energy (BLE) heart rate (HR) sensors with a smartphone application (RITMIA™, Heart Sentinel srl, Italy). In the current study, we test a significant upgrade to the above-mentioned system, thanks to the technical capability of new HR sensors to run algorithms on the sensor itself and to acquire, and store on-board, single-lead ECG strips. We have reprogrammed an HR monitor intended for sports use (Movensense HR+) to run our proprietary RITMIA algorithm code in real-time, based on RR analysis, so that if any type of arrhythmia is detected, it triggers a brief retrospective recording of a single-lead ECG, providing tracings of the specific arrhythmia for later consultation. We report the initial data on the behavior, feasibility, and high diagnostic accuracy of this ultra-low weight customized device for standalone automatic arrhythmia detection and ECG recording, when several types of arrhythmias were simulated under different baseline conditions. Conclusions: The customized device was capable of detecting all types of simulated arrhythmias and correctly triggered a visually interpretable ECG tracing. Future human studies are needed to address real-life accuracy of this device.

Nondestructive Identification of Litchi Downy Blight at Different Stages Based on Spectroscopy Analysis

Litchi downy blight caused by Peronophythora litchii is the most serious disease in litchi production, storage and transportation. Existing disease identification technology has difficulty identifying litchi downy blight sufficiently early, resulting in economic losses. Thus, the use of diffuse reflectance spectroscopy to identify litchi downy blight at different stages of disease, particularly to achieve the early identification of downy blight, is very important. The diffuse reflectance spectral data of litchi fruits inoculated with P. litchii were collected in the wavelength range of 350–1350 nm. According to the duration of inoculation and expert evaluation, they were divided into four categories: healthy, latent, mild and severe. First, the SG smoothing method and derivation method were used to denoise the spectral curves. Then, the wavelength screening methods competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) were compared to verify that the SPA method was more effective. Eleven characteristic wavelengths were selected, accounting for only 1.1% of the original data. Finally, the characteristic wavelengths were tested by six different classification models, and their accuracy was calculated. Among them, the ANN model performed best, with an accuracy of 90.7%. The results showed that diffuse reflectance spectroscopic technology has potential for identifying litchi downy blight at different stages, providing technical support for the subsequent development of related automatic detection devices.

Design and Installation of Fire Sprinkler System in Manufacturing Industry

Fire-sprinkler and other extinguishing systems are intended to extinguish or control the fire. Smoke control systems typically are designed to limit the spread of smoke, which keeps egress routes passable for a given period. The fire alarm, through both the activation of the fire-sprinkler system and manual or automatic detection devices, provides warning to building occupants, as well as notification to emergency personnel responding to the alarm.A fire sprinkler framework is a dynamic fire assurance technique, comprising of a water supply framework, giving sufficient weight and stream rate to a water circulation-channeling framework, onto which fire sprinklers are associated. Also it is utilized as a part of processing plants and substantial business structures, frameworks for homes and little structures are presently accessible at a practical cost. Fire sprinkler frameworks are broadly utilized around the world, with more than 40 million sprinkler heads fitted every year. In structures totally secured by flame sprinklers alone controlled sprinkler frameworks, more than 96% of flames. Sprinklers have been being used in the United States since 1874, and were utilized as a part of manufacturing plant applications where fires when the new century rolled over were regularly disastrous as far as both human and property losses. In the US, sprinklers are today required in all new skyscraper and underground structures for the most part 75 feet (23 m) above or beneath flame office get to, where the capacity of firefighters to give sufficient hose streams to flames is restricted. Sprinklers might be required to be introduced by construction standards, or might be prescribed by insurance agencies to decrease potential property losses or business intrusion. Construction standards in the United States for spots of gathering, by and large more than 100 people, and places with overnight dozing convenience, for example, hotels, nursing homes, residences, and hospitals ordinarily require sprinklers under nearby building regulations, as a state of accepting State and Federal financing. In this Project, the aim is to develop a simple Fire Sprinkler, where the sprinkler can be tested. The test of the sprinkler is to be by connecting the pipes and the reservoir that contains the water and how the breaking of the chemical tube is happening as if there is a real fire occurring in a building. Further studies will include the calculations of the radius and the area that one sprinkler can handle during a fire case and how many one we have to install in a building to cover the fire as a given flow rate and pressure.

A Multipurpose Wearable Sensor-Based System for Weight Training

In recent years, there has been growing interest in automated tracking and detection of sports activities. Researchers have shown that providing activity information to individuals during their exercise routines can greatly help them in achieving their exercise goals. In particular, such information would help them to maximize workout efficiency and prevent overreaching and overtraining. This paper presents the development of a novel multipurpose wearable device for automatic weight detection, activity type recognition, and count repetition in sports activities such as weight training. The device monitors weights and activities by using an inertial measurement unit (IMU), an accelerometer, and three force sensors mounted in a glove, and classifies them by utilizing developed machine learning models. For weight detection purposes, different classifiers including Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), and Multi-layer Perceptron Neural Networks (MLP) were investigated. For activity recognition, the K nearest neighbor (KNN), Decision Tree (DT), Random Forest (RF), and SVM models were trained and examined. Experimental results indicate that the SVM classifier can achieve the highest accuracy for weight detection whereas RF can outperform other classifiers for activity recognition. The results indicate feasibility of developing a wearable device that can provide in-situ accurate information regarding the lifted weight and activity type with minimum physical intervention.

CoviLearn: A Machine Learning Integrated Smart X-Ray Device in Healthcare Cyber-Physical System for Automatic Initial Screening of COVID-19.

The pandemic of novel Coronavirus Disease 2019 (COVID-19) is widespread all over the world causing serious health problems as well as serious impact on the global economy. Reliable and fast testing of the COVID-19 has been a challenge for researchers and healthcare practitioners. In this work, we present a novel machine learning (ML) integrated X-ray device in Healthcare Cyber-Physical System (H-CPS) or smart healthcare framework (called “CoviLearn”) to allow healthcare practitioners to perform automatic initial screening of COVID-19 patients. We propose convolutional neural network (CNN) models of X-ray images integrated into an X-ray device for automatic COVID-19 detection. The proposed CoviLearn device will be useful in detecting if a person is COVID-19 positive or negative by considering the chest X-ray image of individuals. CoviLearn will be useful tool doctors to detect potential COVID-19 infections instantaneously without taking more intrusive healthcare data samples, such as saliva and blood. COVID-19 attacks the endothelium tissues that support respiratory tract, and X-rays images can be used to analyze the health of a patient’s lungs. As all healthcare centers have X-ray machines, it could be possible to use proposed CoviLearn X-rays to test for COVID-19 without the especial test kits. Our proposed automated analysis system CoviLearn which has 98.98% accuracy will be able to save valuable time of medical professionals as the X-ray machines come with a drawback as it needed a radiology expert.

Deep-learning-based anomaly detection for lace defect inspection employing videos in production line

Defect inspection plays an essential role in ensuring quality of industrial products. The most widely used human visual inspection method has some drawbacks such as high cost and low efficiency, which bring an eager demand for the application of automatic defect inspection algorithm in actual production. However, few industrial production lines use automatic detection devices due to the gap between data collected in the actual production environment and ready-made datasets. Lace is one of the industrial products which completely depends on manual defect inspection. The complex and fine texture of lace makes it difficult to extract regular patterns using the existing image-based defect inspection methods. In this paper, we propose to collect lace videos in the weaving stage and design a deep-learning-based anomaly detection framework to detect lace defects. The framework contains three stages, namely video pre-processing stage, pixel reconstruction stage and pixel classification stage. In the offline phase, only defect-free lace videos are needed to train the pixel reconstruction model and calculate the detection threshold by our adaptive thresholding method. In the online phase, the proposed framework reconstructs lace videos and performs defect inspection using reconstruction error and the pre-set threshold. As far as we know, this paper the first to detect fabric defects by videos. Experimental results on artificial defect videos demonstrate the effectiveness of the proposed framework.

Pumpless three-dimensional photo paper-based microfluidic analytical device for automatic detection of thioredoxin-1 using enzyme-linked immunosorbent assay.

Microfluidic-based biosensors have been developed for their precise automatic reaction control. However, these biosensors require external devices that are difficult to transport and use. To overcome this disadvantage, our group made an easy-to-use, cheap, and light pumpless three-dimensional photo paper-based microfluidic analytical device (3D-μPAD; weight: 1.5g). Unlike conventional paper-based microfluidic analytical devices, the 3D-μPAD can be used to control fluid flow in a 3D manner, thus allowing sophisticated multi-step reaction control. This device can control fluid flow speed and direction accurately using only the capillary-driven flow without an external device like a pump. The flow speed is controlled by the width of the microfluidic channel and its surface property. In addition, fluid speed control and 3D-bridge structure enable the control of fluid flow direction. Using these methods, multi-step enzyme-linked immunosorbent assay (ELISA) can be done automatically in sequence by injecting solutions (sample, washing, and enzyme's substrate) at the same time in the 3D-μPAD. All the steps can be performed in 14min, and data can be analyzed immediately. To test this device, thioredoxin-1 (Trx-1), a biomarker of breast cancer, is used as the target. In the 3D-μPAD, it can detect 0-200ng/mL of Trx-1, and the prepared 3D-μPAD Trx-1 sensor displays excellent selectivity. Moreover, by analyzing the concentration of Trx-1 in real patients and healthy individuals' blood serum samples using the 3D-μPAD, and comparing results to ELISA, it can be confirmed that the 3D-μPAD is a good tool for cancer diagnosis.

"Automatic Ingestion Monitor Version 2" - A Novel Wearable Device for Automatic Food Intake Detection and Passive Capture of Food Images.

Use of food image capture and/or wearable sensors for dietary assessment has grown in popularity. "Active" methods rely on the user to take an image of each eating episode. "Passive" methods use wearable cameras that continuously capture images. Most of "passively" captured images are not related to food consumption and may present privacy concerns. In this paper, we propose a novel wearable sensor (Automatic Ingestion Monitor, AIM-2) designed to capture images only during automatically detected eating episodes. The capture method was validated on a dataset collected from 30 volunteers in the community wearing the AIM-2 for 24h in pseudo-free-living and 24h in a free-living environment. The AIM-2 was able to detect food intake over 10-second epochs with a (mean and standard deviation) F1-score of 81.8 ± 10.1%. The accuracy of eating episode detection was 82.7%. Out of a total of 180,570 images captured, 8,929 (4.9%) images belonged to detected eating episodes. Privacy concerns were assessed by a questionnaire on a scale 1-7. Continuous capture had concern value of 5.0 ± 1.6 (concerned) while image capture only during food intake had concern value of 1.9 ±1.7 (not concerned). Results suggest that AIM-2 can provide accurate detection of food intake, reduce the number of images for analysis and alleviate the privacy concerns of the users.

Reconfigurable Hardware Design for Automatic Epilepsy Seizure Detection using EEG Signals

Reconfigurable circuit designs for automatic seizure detection devices are essential to prevent epilepsy affected people from severe injuries and other health-related problems. In this proposed design, an automatic seizure detection algorithm based on the Linear binary Support Vector Machine learning algorithm (LSVM) is developed and implemented in a Field-Programmable Gate Array (FPGA). The experimental results showed that the mean detection accuracy is 86% and sensitivity is 97%. The resource utilization of the implemented design is less when compared to existing hardware implementations. The power consumption of the proposed design is 76mW at 100MHz. The experimental results assure that a physician can make use of this proposed design in detecting seizure events.

Mooring Chain Working State Detection Device

In order to change the current situation that the mooring chain magnetic particle detection is completely completed manually by workers, and improve the detection coverage accuracy, an automatic mooring chain full-ring magnetic particle detection device has been developed to fill the domestic gap. The device is mainly composed of 4 parts: a vertical scanning mechanism, a circular rotation mechanism, a probe linear motion mechanism and a probe adaptive mechanism, which have a total of 6 degrees of freedom. At the same time, ABAQUS finite element analysis software was used to check the static strength, stiffness and dynamic characteristics of the large-span vertical scanning mechanism in the device. The calculation results show that the static strength and stiffness of the vertical scanning mechanism are reliable; the driving pulsation frequency is far from the main excitation frequency, and the structure has good dynamic characteristics.

On Tracer Breakthrough Curve Dataset Size, Shape, and Statistical Distribution

A tracer breakthrough curve (BTC) for each sampling station is the ultimate goal of every quantitative hydrologic tracing study, and dataset size can critically affect the BTC. Groundwater-tracing data obtained using in situ automatic sampling or detection devices may result in very high-density data sets. Data-dense tracer BTCs obtained using in situ devices and stored in dataloggers can result in visually cluttered overlapping data points. The relatively large amounts of data detected by high-frequency settings available on in situ devices and stored in dataloggers ensure that important tracer BTC features, such as data peaks, are not missed. Alternatively, such dense datasets can also be difficult to interpret. Even more difficult, is the application of such dense data sets in solute-transport models that may not be able to adequately reproduce tracer BTC shapes due to the overwhelming mass of data. One solution to the difficulties associated with analyzing, interpreting, and modeling dense data sets is the selective removal of blocks of the data from the total dataset. Although it is possible to arrange to skip blocks of tracer BTC data in a periodic sense (data decimation) so as to lessen the size and density of the dataset, skipping or deleting blocks of data also may result in missing the important features that the high-frequency detection setting efforts were intended to detect. Rather than removing, reducing, or reformulating data overlap, signal filtering and smoothing may be utilized but smoothing errors (e.g., averaging errors, outliers, and potential time shifts) need to be considered. Appropriate probability distributions to tracer BTCs may be used to describe typical tracer BTC shapes, which usually include long tails. Recognizing appropriate probability distributions applicable to tracer BTCs can help in understanding some aspects of the tracer migration.

Safety Assessment of an A-Scan Ultrasonic System for Ophthalmic Use.

This study describes the safety assessment of an A-scan ultrasonic system for ophthalmic use. The system is an investigational medical device for automatic cataract detection and classification. The risk management was based on the International Organization for Standardization (ISO) standard DIN EN ISO 14971:2009-10 and International Electrotechnical Commission (IEC) standard IEC 60601-2-37. The calibration of the ultrasonic field was conducted according to the standards IEC 62127-1:2007 and IEC 62359:2010. The uncertainty on measurements was delineated in agreement with the guide JCGM 100:2008. After risk management, all risks were qualitatively classified as acceptable. The mechanical index (0.08 ± 0.05), soft tissue thermal index (0.08 ± 0.08) and spatial-peak temporal-average intensity (0.56 ± 0.59 mW/cm2 ) were under the maximum index values indicated by the US Food and Drug Administration guidance, Marketing Clearance of Diagnostic Ultrasound Systems and Transducers (0.23, 1, and 17 mW/cm2 , respectively). This study presents a practical approach for the safety assessment of A-scan ultrasonic systems for ophthalmic use. The safety evaluation of a medical device is mandatory before its use in clinical practice. However, the safety monitoring throughout its life cycle should also be considered, since many device components may deteriorate over time and use.